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  • Safari, an O’Reilly Media Company  (193)
  • Spamer, Adolf (1883-1953)
  • [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc.  (193)
  • Electronic videos ; local  (193)
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  • 1
    Online-Ressource
    Online-Ressource
    Spotlight on Innovation : Building Resilient Systems with Heidi Waterhouse (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 57 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: LaunchDarkly’s Heidi Waterhouse shares some of the biggest challenges in feature management. In a perfect world, we’d develop tidy, discrete features that are easy to manage. But in reality, we’re often struggling with a complicated snarl of features that are difficult to test and deploy. Heidi explains how you can use feature management design to build modular, testable, resilient systems. Recorded on April 23, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Innovation , you’ll discover what successful companies have in common and how you can follow their lead with small practical steps to transform your organization and prepare for the Next Economy.
    Anmerkung: Online resource; Title from title screen (viewed September 17, 2019)
    URL: lizenzpflichtig
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    Online-Ressource
    MPI Ethno. Forsch.
  • 2
    Online-Ressource
    Online-Ressource
    InnerSource Product Owners (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 16 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Sponsored by PayPal Middle management support is crucial to the adoption and long term survival of any InnerSource program. But how do you pull that off? In this course, InnerSource expert Silona Bonewald reveals the strategies that will convince middle managers to approve your InnerSource program and support them for doing so. One key to her approach: learn how to measure your program's success. If middle managers can see measurable success, your InnerSource program will thrive. Learners should be familiar with Agile or Lean processes and have a basic understanding of the roles of InnerSource Trusted Committers and Contributors. For best results, please view O'Reilly Media's " Introduction to InnerSource " video before beginning this course. Learn how to build and maintain middle management support for InnerSource programs Understand the Product Owners role in InnerSource Discover the best methods for dealing with escalations and removing bottlenecks Get wise to the numbers your bosses need to see and why they need to see them Explore the best ways to market your team and product to your organization Pick up the ability to create and maintain win/win agreements Learn how to persuade other teams to open their code to your team Silona Bonewald is the Director of InnerSource Engineering at Paypal. A developer and open source advocate since the late 90s, Silonia is a frequent contributor at O'Reilly OSCON conferences, where she speaks about InnerSource and other open source related topics.
    Anmerkung: Online resource; Title from title screen (viewed May 13, 2019)
    URL: lizenzpflichtig
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  • 3
    Online-Ressource
    Online-Ressource
    Artificial Intelligence Conference 2019 - Beijing, China (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 3 hr., 26 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: A unique keynote speech by Yangqing Jia, the leader of Alibaba’s AI and Big Data organization. A revealing talk by Ion Stoica (UC Berkeley) describing some the AI projects underway at UC Berekely's renowned RISELab. A thoughtful presentation by Long Wang, VP of TenCent Cloud, illustrating what TenCent believes AI will do for the cloud. These are just three of the illuminating talks given by the many AI experts from China, the U.S., and elsewhere who gathered to speak at O'Reilly Media's Artificial Intelligence Conference Beijing June 2019. This video compilation gives you the opportunity to see all of the best presentations from AI Beijing—it contains more than 60 hours of material to review on your schedule. AI Beijing's unique focus is applied AI—bridging the gap between AI developments in research and their commercial applications in business and industry. If you want to understand how AI will change the business landscape, or are working with deep learning or AI (or plan to be)— this video compilation is for you. Highlights include: Complete video recordings from the best of AI Beijing June 2019—this video compilation contains hours of material to review at your own schedule. 55% of the presentations are in English, 42% are in Chinese, and 3% are in English and Chinese. Keynote speeches by AI thought leaders such as Maria Zhang (LinkedIn), Pete Warden (Google Brain Team), Michael James (Cerebras), Hao Zheng (PlusAI), and Tim Kraska (MIT). Tutorials in Chinese, including Intels' Zhen Zhao's exploration into the OpenVINO toolkit and Microsoft's Henry Zeng, Lu Zhang, and Xiao Zhang's introduction to automated machine learning using Python and AutoML. Tutorials in English, such as Alejandro Saucedo's (The Institute for Ethical AI & Machine Learning) practical guide to de-mystifying machine learning bias and Richard Liaw (UC Berkeley RISELab) on how to build reinforcement learning models and AI applications with Ray. "Implementing AI" sessions presented in Chinese, such as Zhenxiao Luo (Uber) on how to run big data and machine learning systems at scale; Hui Xue (Microsoft) on the latest automated machine learning advances at Microsoft Research Asia; and Guoqiong Song (Intel) and Luyang Wang (Office Depot) on how Home Depot's real-time recommendation system was built using Analytics Zoo on BigDL and Apache Spark "Implementing AI sessions" delivered in English, including Google's Kaz Sato's examination of ML operations and Kubeflow pipelines; Rak...
    Anmerkung: Online resource; Title from title screen (viewed June 28, 2019)
    URL: lizenzpflichtig
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  • 4
    Online-Ressource
    Online-Ressource
    Taking graph applications to production (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 41 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Over the past decade, Denise Gosnell (DataStax) has helped build some of the largest production applications of graph databases around the world. From those experiences, she’s collected a set of common areas in which teams frequently misstep when getting started with graph technology. It also happens that those themes parallel the experience of playing one of her favorite games, SimCity 2000. Denise walks you through a few of these topics. Know the rules. The introduction of graph data into your application introduces a new paradigm of data modeling: relationship-first design instead of entity-first design. The transition to relationship-first design principles introduces a new set of rules to consider for understanding your application’s performance, just like learning the rules of building a successful metropolis in SimCity . In this section, you’ll dive into the computational overhead introduced into your system from the branching factor and selectivity of your graph traversals. Things can quickly become catastrophic. Relationship-first data modeling can create a sleeping time bomb in your graph data: namely, supernodes. Just like in SimCity , high volumes of progress without proper planning will eventually introduce a catastrophe. To plan for this, you will need to track, mitigate, and eliminate the potential for supernodes within your applications. In this section, Denise introduces supernodes and presents tangible plans for avoiding the disasters which they can create. You’re going to make mistakes. Just like the learning process for understanding the tools and rules for building a successful city, you’ll inevitably make some mistakes when starting down the path of integrating graph technology into your stack. These common mistakes often start out as red herrings that are misinterpreted as graph problems. In this section, you’ll explore three use cases that are frequently misinterpreted as graph problems and learn techniques for avoiding these traps. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
    URL: lizenzpflichtig
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  • 5
    Online-Ressource
    Online-Ressource
    Spotlight on Data : Caching Big Data for Machine Learning at Uber with Zhenxiao Luo (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 1 hr., 1 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: From determining the most convenient rider pickup points to predicting the fastest routes, Uber uses data-driven machine learning to create seamless trip experiences. Within engineering, big data and machine learning inform decision-making processes across the board. As Uber expands to new markets, the ability to accurately and quickly use data to make predictions becomes even more important. In this case study, Uber’s Zhenxiao Luo details the company's machine learning architecture and talk about how Uber uses big data to power machine learning jobs. Recorded on June 17, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Data , you’ll learn about, discuss, and debate the tools, techniques, questions, and quandaries in the world of data. You’ll discover how successful companies leverage data effectively and how you can follow their lead to transform your organization and prepare for the Next Economy.
    Anmerkung: Online resource; Title from title screen (viewed October 4, 2019)
    URL: lizenzpflichtig
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 6
    Online-Ressource
    Online-Ressource
    Security principles for the working architect (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 51 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Security is an ever more important topic for system designers. As our world becomes digital, today’s safely hidden back office system is tomorrow’s public API, open to anyone on the internet with a hacking tool and time on their hands. So the days of hoping that security is someone else’s problem are over. The security community has developed a well-understood set of principles used to build systems that are secure (or at least securable) by design, but this topic often isn’t included in the training of software developers, who assume that it’s only relevant to security specialists. Even when principles are explained, they’re often shrouded in the jargon of the security engineering community, and so mainstream developers struggle to understand and apply them. Eoin Woods (Endava) explains why secure design matters and then introduces a set of 10 of the most important proven principles for designing secure systems, distilled from the wisdom of the security engineering community. He discusses each principle in the context of mainstream system design, rather than in the specialized language of security engineering, focusing on how it’s applied in practice to improve security. This session was recorded at the 2019 O'Reilly Software Architecture Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 7
    Online-Ressource
    Online-Ressource
    O'Reilly Software Architecture Conference 2019 - San Jose, California (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 63 hr., 23 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: The O'Reilly Software Architecture Conference San Jose 2019 (SACON) gathered the world's leading software architects and engineers to give presentations on software architecture's most useful technologies, trends, and techniques. This video compilation gives you complete access to the best of SACON's keynotes, tutorials, and technical sessions. It contains hours of material to review and study at your own pace. Whether you are optimizing legacy systems or migrating to cloud native architecture, this compilation from SACON San Jose 2019 offers you the insights and training you need to get to the next level. Highlights include: Contains hours of video recordings from the best of SACON San Jose 2019's keynotes, tutorials, and technical sessions. 3.5 hour tutorials like Christian Hernandez's (Red Hat) hands-on introduction to Kubernetes and OpenShift; Nathaniel Schutta's (Pivotal) deep dive into trade-off analysis and how to use it to strategically choose the correct technology for your projects; and Valentina Rodriguez's (Independent) reveal of the twelve essential principles of architecture design in Agile environments. Keynotes from Rebecca Wirfs-Brock (Wirfs-Brock Associates), Michael Feathers (R7K Research and Conveyance), Adam Tornhill (Empear), Rebecca Parsons (ThoughtWorks), and Neal Ford (ThoughtWorks). Application Architecture sessions, such as Stefania Stefansdottir's (ThoughtWorks) walk through of the practices all new architects and tech leads should keep in mind when starting a new project; Ian Varley (Salesforce) on spotting and correcting the cognitive biases that undermine your software architecture; and Andrew Bonham and Thiagarajan Subramanian's (Capital One) review of how to use reactive architecture and microservices, machine learning, H20, Akka, and Kafka. Microservices sessions, including Kasun Indrasiri's (WSO2) in-depth overview of common microservice resiliency patterns such as timeout, retry, circuit breaker, fail-fast, bulkhead, transactions, and more; Nathaniel Schutta (Pivotal) on the factors used to decide if something deserves to be a microservice or not; and Samir Behara (EBSCO) on building scalable microservice architectures with Envoy, Kubernetes, and Isti. Enterprise Architecture sessions, including Heidi Waterhouse (Launch Darkly) on the smartest ways to achieve build-versus-buy decisions; Cat Swetel (Ticketmaster) on using value network mapping and real options theory to evolve monolithic software; and Paula ...
    Anmerkung: Online resource; Title from title screen (viewed June 13, 2019)
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  • 8
    Online-Ressource
    Online-Ressource
    Leveraging data science in asset management (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 42 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Asset managers of all stripes are ramping up their data science capabilities. Andrew Chin and Celia Chen (AllianceBernstein) offer an overview of data science applications within the asset management industry, covering case studies spanning different functions within an asset management organization. Along the way, they look at an investment research project that used a new “big dataset” as well as a machine algorithm to create a more powerful prediction model and detail a machine learning model they developed to understand their clients better. This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
    URL: lizenzpflichtig
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  • 9
    Online-Ressource
    Online-Ressource
    Learning from failure : Why a total site outage can be a good thing (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 34 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Although an outage is a terrifying prospect, you should embrace it as an opportunity. Failure can expand and improve your understanding of your systems. Three years ago, Indeed suffered one of the worst outages in its history. No single fault or failure caused this outage. Rather, it was a complex interaction of bugs, design decisions, capacity loss, and poor situational awareness during incident response. Indeed learned valuable lessons from this event. It identified ways to make the systems more resilient and improved the approach to the incident lifecycle within the engineering culture. Alex Elman uses the narrative of this incident to demonstrate how a site-wide outage can inform increased resilience and reduced operational complexity. Learning from failure is a feedback loop rather than a one-off process. He applies Indeed’s outage as a practical example of what an iteration of this loop can look like. He shares with other SREs the success that has risen from this failure. Indeed hasn’t had a global site outage in the three years since this event. Alex begins with a discussion of failure to set the stage for delivering the incident background, then discusses incident response and situational awareness. He explains conducting incident postmortems and learning from failure and designing for reliability, including resilience patterns such as circuit breaking and graceful degradation. Finally, he gets into resilience testing, running chaos tests, and closing the feedback loop, leaving some time for a question and answer session. This session was recorded at the 2019 O'Reilly Velocity Conference in San Jose.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
    URL: lizenzpflichtig
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  • 10
    Online-Ressource
    Online-Ressource
    O'Reilly TensorFlow World 2019 - Santa Clara, CA (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 2 hr., 46 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: We’ll make the Keynotes available here as soon as possible after they happen. Video of the sessions and tutorials will be available a few weeks after the end of the conference. Open source TensorFlow 2.0 is driving the machine learning (ML) revolution around the globe. The TensorFlow World Conference Santa Clara 2019—the first international conference devoted to TensorFlow—provided the thousands who attended the conference with an extraordinary opportunity to see TensorFlow 2.0 in action, discover new ways to use it, and learn how to successfully implement it in their own enterprises. This video compilation offers you the chance to experience the TensorFlow World Conference yourself. If you get it and explore it, you’ll come away with a firm understanding of the entire machine learning stack, TensorFlow 2.0, and the reasons why companies like Spotify, LinkedIn, Amazon, Twitter, and Uber use TensorFlow to solve complex business problems. Highlights include: A front row view for all of the best keynotes, tutorials, and technical sessions from the TensorFlow World Conference Santa Clara 2019. Complete presentations from some of the world’s top TensorFlow practitioners, including talks by the people and teams who developed TensorFlow. Keynote addresses from TensorFlow’s leaders, such as Google Brain co-founder Jeff Dean; Theodore Summe, the head of product for Cortex, Twitter’s central ML organization; and Megan Kacholia, VP of Engineering for Google Research. Deep-dive tutorials, including Laurence Moroney’s (Google Brain) primer on ML with TensorFlow; Sandeep Gupta’s (Google) review of ML in JavaScript using TensorFlow.js; and Neelima Mukiri’s (Cisco) intro to model building and optimization for TensorFlow in any Kubernetes environment. Applications sessions focusing on real-world TensorFlow implementations, like Asif Hasan’s (Quantiphi) talk on using ML to both predict cancer recurrence and recommend treatment; Bhushan Jagyasi’s (Accenture) survey of TensorFlow successes in banking and insurance; and Hamel Husain’s (GitHub) review of automating developer workflows on GitHub with TensorFlow. Core Technologies sessions, where you’ll hear directly from TensorFlow team members such as Paige Bailey (Google) on TensorFlow Swift, a next-generation ML platform; Raziel Alverez (Google) on TensorFlow model optimization techniques; and Robby Neale (Google) on how to build models with tf.text. Accelerators sessions, including Victoria Rege (Graphcore) on h...
    Anmerkung: Online resource; Title from title screen (viewed November 1, 2019)
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  • 11
    Online-Ressource
    Online-Ressource
    O'Reilly Artificial Intelligence Conference 2019 - San Jose, California (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 91 hr., 19 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: The O’Reilly Artificial Intelligence Conference San Jose 2019 was some of the world’s top AI practitioners sharing their AI passion and AI knowledge with thousands of attendees. It was Uber AI Lab’s Kenneth Stanley illuminating the future of AI with his talk about open-endedness learning. It was Danny Lange (Unity Technologies) on game environments that test the capabilities of AI-trained agents; Yi Zhang (University of California, Santa Cruz) on chatbots and the nearness of true conversational computing; and Hagay Lupesko (Facebook) on the challenges of mega-scale, deep learning-based personalization modeling. In short, AI San Jose 2019 was a mind-blower and this video compilation gives you access to virtually all of it with hours of material to peruse, study, and absorb on your own schedule. Highlights include: Complete video recordings of the best of AI San Jose 2019’s keynote addresses, deep dive tutorials, and technical sessions. Keynote addresses from AI thought leaders such as Andrew Feldman (Cerebras Systems), Sahika Genc (AWS DeepRacer/SageMaker RL), and Mike Jordan (UC Berkeley). Unrestricted access to the exclusive AI Business Summit’s executive briefings, best practice sessions, and tutorials led by AI business pros such as Michael Radwin (Intuit), Bahman Bahmani (Rakuten), Mayukh Bhaowal (Salesforce Einstein), Yael Gozin (Pfizer), and James Manyika (McKinsey & Company). Deep dive tutorials, including Jason Dai (Intel) on building deep learning apps for big data with the Analytics Zoo AI platform; Chaoran Yu (Lightbend) on doing machine learning (ML) with Kafka-based streaming pipelines; and Justina Petraityte (Rasa) on developing intelligent AI assistants based entirely on ML with open source Rasa NLU and Rasa Core. Sessions devoted to AI Implementation, such as Anuradha Gali (Uber) on using AI to leverage 15 million trips a day on the Uber platform; Roshan Sumbaly (Facebook) on connecting the dots between the software engineering and ML development worlds; Paige Bailey’s (Google) on TensorFlow 2.0's new features; and Alex Ratner (Snorkel) on building and managing training datasets for ML with open source Snorkel. Sessions focused on AI Models & Methods, including Lukas Biewald (Weights & Biases) review of how to use Keras to classify text with LSTMs and other ML techniques; and Francesca Lazzeri (Microsoft) on using AutoML to automate ML model selection and hyperparameter tuning. Dozens of how-to-do-it sessions detailing the tec...
    Anmerkung: Online resource; Title from title screen (viewed September 11, 2019)
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  • 12
    Online-Ressource
    Online-Ressource
    How to build privacy and security into deep learning models (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 38 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: In recent years, we’ve seen tremendous improvements in artificial intelligence, due to the advances of neural-based models. However, the more popular these algorithms and techniques get, the more serious the consequences of data and user privacy. These issues will drastically impact the future of AI research—specifically how neural-based models are developed, deployed, and evaluated. Yishay Carmiel (IntelligentWire) shares techniques and explains how data privacy will impact machine learning development and how future training and inference will be affected. Yishay first dives into why training on private data should be addressed, federated learning, and differential privacy. He then discusses why inference on private data should be addressed, homomorphic encryption and neural networks, a polynomial approximation of neural networks, protecting data in neural networks, data reconstruction from neural networks, and methods and techniques to secure data reconstruction from neural networks. This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 13
    Online-Ressource
    Online-Ressource
    Cloud native data pipelines with Apache Kafka (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 43 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: As microservices, data services, and serverless APIs proliferate in a cloud native world, analysts still need to report on the business as a whole. Data engineers need to collect and standardize data in an increasingly complex and diverse system. Luckily, the problem is also the solution. The way to manage data in a cloud native environment is to build cloud native data pipelines. Gwen Shapira (Confluent) discusses how data engineering requirements have changed in a cloud native world and how the solutions have changed with them. She then shares architectural patterns that are commonly used to build cloud native data infrastructure and explains how they help you build flexible, scalable, and reliable pipelines to give your business visibility on all your data. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 14
    Online-Ressource
    Online-Ressource
    Automating DevOps for machine learning (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 40 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: You’ve spent hundreds of hours cleaning your data, engineering features, and training and tuning your model to pinpoint accuracy. But now it’s time to deploy your model into production. Whether you lead a team of data scientists or are one yourself, you know how time-consuming and problematic deployment can be. Language and environment incompatibilities, manual and duplicative processes, out-of-control costs, and poor communication can destroy all the work you’ve put into building models and slow your machine learning efforts. Learning common deployment architectures for machine learning in the real world and understanding how to build your model for a production environment can help you avoid pitfalls when scaling up. Diego Oppenheimer (Algorithmia) discusses common problems and solutions and shares best practices from leading organizations that have solved the deployment headache. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 15
    Online-Ressource
    Online-Ressource
    Artificial intelligence on human behavior : New insights into customer segmentation (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 35 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Brands, marketers, and product designers need to understand their customers. Traditionally, market research was driven by surveys and focus groups of limited scale. Today, digital signals like web browsing behavior can provide a stream of observed behavioral data that is rich with information about a user’s interests, needs, and preferences. This type of data, coupled with machine learning techniques, holds the promise of freeing market research from the constraints of self-reported customer data. In its raw state, web browsing data is both too detailed and too sparse to be comprehensible, let alone actionable. Melinda Han Williams (Dstillery) explores semantic embeddings as a novel approach for understanding observed digital consumer behavior and details how to use a semantic embedding of web browsing behavior to drive unsupervised clustering for customer segmentation. You’ll learn how Dstillery has trained a neural network on 15 billion behavioral interactions. The resulting model can be seen as a much lower dimensional embedding of the internet and, if projected into two or three dimensions, as an interactive map. This taxonomy of internet behavior can be used as the foundation for a number of applications, providing unparalleled insights into consumer behavior and needs. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 16
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    7 years of DDD : Tackling complexity in large-scale marketing systems (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 49 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: It turns out that domain-driven design is not just for cargo shipping. Vladik Khononov (Naxex) explains how he and his team embraced domain-driven design (DDD) (with very limited resources and a very short time to market) at Plexop, a large-scale marketing system that spans over a dozen different business domains. Join in to learn how DDD allowed the team to manage business complexities, find out strategies for defining context boundaries, explore lessons learned the hard way, and discover where they had to adapt the DDD methodology to fit the company’s needs. This session was recorded at the 2019 O'Reilly Software Architecture Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 17
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    Spotlight on Innovation : Succeeding with Machine Learning with Alex Jaimes (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 58 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Machine learning (ML) is absolutely critical to driving your business success—and more opportunities to integrate it in innovative ways are emerging every day. However, implementation isn’t always as easy as it seems. To effectively execute ML and make it work for your organization, you must solve for this new interdependency between data, metrics, and testing. In this Spotlight on Innovation , learn about machine learning opportunities and challenges—and why process improvements can be even more beneficial than algorithmic ones. Recorded on February 13, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Innovation , you’ll discover what successful companies have in common and how you can follow their lead with small practical steps to transform your organization and prepare for the Next Economy.
    Anmerkung: Online resource; Title from title screen (viewed July 30, 2019)
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  • 18
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    Spotlight on Data : Data Storytelling with Mico Yuk (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 56 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: The power of storytelling has long been used in Hollywood to engage viewers and generate billions of dollars in sales. However, data storytelling, while highly trendy, is frequently misused and often limited to pretty charts. In this Spotlight on Data , learn how to use the power of storytelling with your data to skyrocket user engagement. Get your users to take action by implementing a few proven techniques. Recorded on July 15, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Data , you’ll learn about, discuss, and debate the tools, techniques, questions, and quandaries in the world of data. You’ll discover how successful companies leverage data effectively and how you can follow their lead to transform your organization and prepare for the Next Economy.
    Anmerkung: Online resource; Title from title screen (viewed July 30, 2019)
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  • 19
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    Sooner than you think : Neural interfaces are finally here (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 46 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: During the past decade, significant advancements in artificial intelligence and machine learning have expanded how we communicate and interact with the world. There are more screens and devices in our lives, but our ability to control these devices hasn’t kept pace. While controversial projects involving invasive brain-machine interfaces have been grabbing headlines, this technology is still decades away from exiting the lab and changing how we interact with the digital world for the better. Patrick Kaifosh outlines a future where we’ll be looking up at the world instead of down at our phones. With the launch of the CTRL-labs developer kit (CTRL-kit), noninvasive neural interfaces are finally here, and the possibilities are almost limitless. This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 20
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    Recommendation engines and mobile gaming (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 41 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Mobile gaming is a $50+ billion industry. Much of the industry’s growth has been fueled by the sale of in-game virtual resources and items to help players progress further or improve their overall gaming experience. One of the biggest concerns for mobile gaming developers is improving their overall monetization without getting in the way of players enjoying the game. KIXEYE—a developer of complex mobile strategy games—periodically provides its player base with a handful of in-app purchase options that provide different in-game content at different price points and discounts. The problem that companies run into using this model is what in-app purchases should be shown and when in order to maximize the number of in-app purchases. This is made more difficult at KIXEYE due to the massive number of in-app purchases available in the company’s games. So how do you solve this problem? Bysshe Easton and Thomas Dobbs explain how KIXEYE used hybrid recommendation engine techniques to create personalized in-app purchase recommendations for its customers, resulting in a 20%+ lift in user revenue. Along the way, they cover some parallelization techniques the company used to nearly eliminate scaling issues. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 21
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    Random search and reproducibility for neural architecture search (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 41 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Neural architecture search (NAS) is a promising research direction that has the potential to replace expert-designed networks with learned, task-specific architectures. Ameet Talwalkar (Carnegie Mellon University Determined AI) shares work that aims to help ground the empirical results in this field and proposes new NAS baselines that build off the following observations: NAS is a specialized hyperparameter optimization problem, and random search is a competitive baseline for hyperparameter optimization. Leveraging these observations, Ameet evaluates both random search with early-stopping and a novel random search with a weight-sharing algorithm on two standard NAS benchmarks: PTB and CIFAR-10. Results show that random search with early-stopping is a competitive NAS baseline that performs at least as well as ENAS, a leading NAS method, on both benchmarks. Additionally, random search with weight-sharing outperforms random search with early-stopping, achieving a state-of-the-art NAS result on PTB and a highly competitive result on CIFAR-10. Ameet concludes by exploring existing reproducibility issues for published NAS results, noting the lack of source material needed to exactly reproduce these results, and discussing the robustness of published results given the various sources of variability in NAS experimental setups. All information (code, random seeds, documentation) needed to exactly reproduce our results will be shared, along with random search with weight-sharing results for each benchmark on two independent experimental runs. This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 22
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    OSCON 2019 - Portland, Oregon (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 160 hr., 43 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local ; Electronic videos
    Kurzfassung: OSCON Portland 2019 brought together a vibrant and diverse collection of talented speakers (open source leaders from around the globe) who do amazing things with open source technologies. This outstanding group provided the conference attendees (thousands of software developers, programmers, architects, engineers, CxOs, hackers, geeks, and analysts) with the opportunity to explore the latest open source tools and technologies; get expert in-depth training in crucial languages, frameworks, and best practices; and gain exposure to the open source stack in all its possible configurations. This video compilation offers you the chance to see and hear the best of OSCON Portland 2019. If you want to know how to build an open source culture at your company, work in a cloud environment that isn't always open source-friendly, understand how machine learning can make or break your code, or implement new technologies like Kubernetes and TensorFlow, then getting the OSCON Portland 2019 video compilation is for you. Highlights include: Complete video recordings of OSCON Portland 2019’s best keynotes, tutorials, and tech sessions offering hours of material to study and absorb at your own pace and schedule Keynotes, including Adam Jacob (Chef) on the war for the soul of open source; Kay Williams (Microsoft Azure) on lessons learned building a strong Kubernetes/VSCode open source community; Adrian Cockcroft (AWS) on leveraging cloud vendors to boost open source business success; and Pete Skomoroch (Workday) on the urgent need for a new open source ML development stack Tutorials offering deep dives into open source tech like Apache Kafka, Rust, extended Berkeley Packet Filters, Spring/Spring Boot, Ethereum DApps, Jenkins 2, Vault/Kubernetes, and Kubeflow The OSCON Business Summit: sessions offering an insider’s look at the open source implementations that have the most profound impact on business with talks by open source specialists at Uber, Pacific Life, Verizon Media, The Home Depot, Baidu, the BBC, and more Open Source do-it-now sessions, including Deb Nicholson’s (Software Freedom Conservancy) whirlwind tour of what not to do when running an open source project; Russell Rutledge’s (Nike) take on how to build an open source culture at your company; and Angie Jones (Applitools) and a panel of experts from Persea Consulting, Red Hat, Magento, and ForgeRock on the best ways to build and maintain open source communities Emerging Languages and Frameworks session...
    Anmerkung: Online resource; Title from title screen (viewed July 17, 2019)
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  • 23
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    O'Reilly Strata Data Conference 2019 - New York, New York (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 149 hr., 54 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: The 2019 Strata Data Conference NYC, the biggest Big Data conference in the world, was a massive success. Packed with thousands of attendees, Strata gathered the world's top data practitioners to provide expert guidance on the tools and technologies you need to make your data strategies and projects work today. This video compilation holds the best of Strata NYC 2019's keynotes, tutorials, and technical sessions. Looking for a head start on the data techniques and technologies you need to succeed? This compilation points the way forward by offering you hours of material to study and absorb at your own rate. The compilation includes such gems as Data Engineering and Architecture sessions, where you'll learn how to select the right type of data infrastructure and architecture to streamline your workflows, reduce costs, and scale your data analysis; Data Science, Machine Learning (ML) and AI sessions, where you'll learn how to use text mining, real-time analytics, large-scale anomaly detection and other techniques to discover the hidden insights in your data; and all of the best talks from the Strata Business Summit, where you'll receive an insiders-only look at the processes and technologies some of the world's most successful companies used to develop their own data strategies. Highlights include: A front row seat at 2019's Strata Data Conference NYC best keynotes, tutorials, and technical sessions—contains hundreds of hours of material to study and absorb at your own pace. Keynote speeches from Big Data's most inspiring business visionaries, such as Sara Menker (CEO, Gro Intelligence), Cassie Kozyrkov (Chief Decision Scientist, Google Cloud), Swatee Singh (VP Big Data/ML, American Express), and Robert D. Thomas (GM, IBM Data and AI). Deep dive tutorials including Jules Damji's (Databricks) sold out session on managing the complete ML lifecycle with MLflow; Karthik Ramasamy's (Streamlio) review of serverless streaming architectures and algorithms for the enterprise; and Mark Donsky (Okera) on how to secure your data lakes to meet the rigors of CCPA privacy regulations. Data Engineering and Architecture sessions, including Navinder Pal Singh Brar (Walmart Labs) on building multitenant data processing and model inferencing platforms with Kafka Streams; Paige Roberts (Vertica) on the whys and hows of putting large stateful applications into containers and Kubernetes; Tomer Levi (Fundbox) on using AWS Step Functions, Docker containers, and ECS Farg...
    Anmerkung: Online resource; Title from title screen (viewed September 25, 2019)
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  • 24
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    O'Reilly Artificial Intelligence Conference 2019 - London, United Kingdom (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 2 hr., 37 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: We’ll make the Keynotes available here as soon as possible after they happen. Video of the sessions and tutorials will be available a few weeks after the end of the conference. The Artificial Intelligence Conference London 2019 gathered some of the globe's top AI practitioners to speak about AI's most promising developments, emerging technologies, and profitable use cases. This video compilation provides you with the best that AI London 2019 had to offer. It includes thought provoking presentations from such AI luminaries as Ariadna Font Llitjós, the director of engineering at Twitter’s Cortex Machine Learning Platform; Martin Goodson, the chief scientist at Evolution AI; Jeff Jonas, the founder and CEO of Senzing; Kim Hazelwood, the senior engineering manager who leads AI infrastructure research efforts at Facebook, and many more of AI’s top data scientists, software engineers, and business strategists. AI is changing fast and it’s transforming business even faster. To see where AI is going (and the entirely new business models and procedures it enables), get this compilation and you’ll be ahead of the curve. Highlights include: Complete video recordings of AI London 2019’s best keynotes, tutorials, and technical sessions—this compilation contains hours of material to study, review, and absorb at your own pace. Keynote presentations from AI’s most notable thinkers, including Intels’ Alexis Crowell Helzer, IBM’s Ritika Gunnar, Dell Technologies’ Arash Ghazanfari, and more. Hours of fact-packed AI, ML, and DL tutorials from top AI practitioners like Robert Crowe (Google) on ML pipelines, TensorFlow Extended pipelines, and ML production deployment issues; Danielle Dean (iRobot) on training and deploying Python models in Azure; and Sergey Ermolin (AWS) on how to use reinforcement learning to build recommendation systems with AWS SageMaker RL. Members-only access to every AI Business Summit Executive Briefing/Best Practices session: Twenty hard-nosed presentations that provide an insider’s look at the AI implementations that will impact your business the most. Implementing AI sessions, including Carlos Rodrigues's (Siemens) look at how Siemens fights cybercrime with AI; Alex Ingerman's (Google) introduction to Federated ML, a new decentralized form of ML; Siddha Ganju's (NVIDIA) discussion of how to set-up deep learning on mobile devices; and Thomas Phelan (HPE BlueData) on how to spin up GPU-enabled AI, ML, and DL clusters in Docker containers...
    Anmerkung: Online resource; Title from title screen (viewed October 16, 2019)
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  • 25
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    NLP from scratch : Solving the cold start problem for natural language processing (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 43 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Unstructured data in the form of documents, web pages, and social media interactions is an ever-growing, ever-more valuable data source for addressing present business problems, from exploring brand sentiment to identifying sensitive information in internal documents. Unfortunately, the classification and annotation algorithms behind solving these problems often require significant amounts of labeled training data to produce desired accuracy. Michael Johnson and Norris Heintzelman (Lockheed Martin) share several techniques they’ve implemented to build classification and NER models from scratch. They lead a tour through this space as it applies to NLP and demonstrate their approach and architecture for the following techniques: Weak supervision for news documents: Using rules base classification alongside deep learning system for text classification Active learning and human in the loop: Explaining how breakthroughs in transfer learning for NLP have impacted their active learning framework for building an LSTM-based relevance model Creative training sets: Identifying and cleaning already-labeled datasets, training classifier on “only” positive examples NER adjudication: Combining knowledge from several annotation sources that leverages the strengths of each source For each of these topics, Michael and Norris outline the theoretical foundation, the implementation architecture, and tools used and discuss the problems they encountered—so you can avoid making the same mistakes.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 26
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    Microservices architecture in the real world (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 50 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Over the past two years, Credit Karma has gone from zero to nearly 100 microservices, supporting over 300 engineers while serving our more than 80 million members. Drawing on his experience at Credit Karma, Mason Jones shares approaches based on his real-world experiences making the shift to microservices, covering routing (How does service A find and call service B?), management (How do you handle hundreds of containers?), observability (How do you know what’s going on out there?), and experience (How do your developers deal with these services?), as well as the cultural and organizational impact that you can’t avoid. Join in to explore solutions, pitfalls, and practical examples that have worked in the real world at scale. This session was recorded at the 2019 O'Reilly Software Architecture Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 27
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    Learning from multiagent emergent behaviors in a simulated environment (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 44 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Traditionally, determining the most efficient designs and practices—whether for determining how store merchandise should be arranged or where people and machines should be laid out in a factory floor—has required vast amounts of data and human assessment. These efficient designs can be the difference between a thriving company and a struggling one. Recent advancements in multiagent reinforcement learning within virtual environments, such as DeepMind’s Capture the Flag or Open AI’s Learning to Compete and Cooperate, have led to a novel approach for tackling efficient design and practices. Danny Lange (Unity Technologies) explains how observing emergent behaviors of multiple AI agents in a simulated virtual environment can lead to the most optimal designs and real-world practices, all without introducing human bias or the need for vast amounts of data. This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 28
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    Choreographing microservices (NY) (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 1 hr., 29 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Choreographed microservices talk to each other asynchronously, blindly broadcasting notifications into a service cloud. Those notifications are handled by whatever client services are interested. These systems eliminate many of the problems associated with orchestrated systems (which work more like synchronous function calls) and are typically much faster than orchestrated systems, but they have their own idiosyncrasies and implementation challenges. Allen Holub explores the inherent problems in orchestrated systems and then looks at how choreography can solve those problems. Allen explores three approaches to choreography: HTTP based, pub/sub messaging based, and brokerless swarming systems. He introduces appropriate messaging architectures and frameworks and looks at several practical examples. Finally, Allen looks at event storming: one of the best approaches to designing choreographed systems. You’ll leave with an understanding of both why you should be using choreography for most high-level APIs and how to design and build these systems. This session was recorded at the 2019 O'Reilly Software Architecture Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 29
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    Artificial Intelligence Conference 2019 - New York, New York (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 75 hr., 47 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Why did companies like Intuit, JP Morgan Chase, MasterCard, and BuzzFeed deploy AI and what business advantages have they already reaped from those deployments? How have the toolsets recently developed at Google (BERT) and Microsoft (Project Brainwave) opened up AI as a mainstream business reality? How does IBM's AI Fairness 360 toolkit combat the very real problem of unwanted bias in AI applications? You'll find the answers to these questions and many more in this video compilation of the best talks from AI New York 2019. Containing hours of material to explore at your own pace, this video compilation provides an insider's view of the latest developments in AI. Highlights include:M/p〉 Complete video recordings of the talks delivered at AI NY 2019 by 170 of the world's top AI experts. Keynote addresses from AI's best thinkers, such as MIT's Aleksander Madry, Intuit's Desiree Gosby, Princeton University's Olga Troyanskaya, Netflix's Tony Jebara, Stanford University's Christopher Ré, Facebook's Kim Hazelwood, Carnegie Mellon University's Martial Hebert, and Primer's Sean Gourley, plus a look at Dell's "Sophia", the world’s first robot citizen. All of the Executive Briefings and detailed case studies from the exclusive AI Business Summit, including Kristian Hammond's (Northwestern Computer Science) day long tutorial offering a practical framework for bringing AI into your company; Adam Cheyer's (Samsung) look at how AI enables a totally new form of software development where humans and machines work collaboratively together; and Jennifer Fernick's (NCC Group) learned predictions of the industries that will benefit from the coming intersection of quantum computing, machine learning, and AI. Tutorials by AI's most experienced practitioners, including Gunnar Carlsson (Stanford University) on using topological data analysis to understand, build, and improve neural networks; Bruno Goncalves (JPMorgan Chase) on using recurrent neural networks for time series analysis; and Mo Patel (Independent) on how to build machine learning models in PyTorch. Sessions focused on machine learning, including Alina Matyukhina's (Canadian Institute for Cybersecurity) reveal of the methods dishonest actors use ML to mimic the coding style of software developers in open source projects; Chakri Cherukuri's (Bloomberg LP) discussion of how to apply machine learning and deep learning techniques in quantitative finance; and Cibele Montez Halasz's (Twitter) description of time...
    Anmerkung: Online resource; Title from title screen (viewed April 17, 2019)
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    Spotlight on Innovation : Predicting the Next Big Breakthrough with Nima Montazeri (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 57 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: With new technologies increasing the pace of innovation every day, companies demand a constant supply of fresh ideas. To be successful, an innovation process must deliver two things: a superior solution and a great market response. Once you have the former, how do you ensure the latter? It all starts by examining the key dimensions of a technology’s evolution and understanding how it’s delighting customers. Learn how to make better decisions about where to focus your innovation efforts and money. You’ll also find out—through two case studies—how backing the right industry innovations can pay off for your organization in a major way. Recorded on April 10, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Innovation , you’ll discover what successful companies have in common and how you can follow their lead with small practical steps to transform your organization and prepare for the Next Economy.
    Anmerkung: Online resource; Title from title screen (viewed August 29, 2019)
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    Spotlight on Cloud : Becoming Cloud Native with Jon Collins (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 1 hr., 0 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Startups seem to come out of nowhere, disrupting their markets and leaving traditional organizations to ask themselves how they too can adopt cloud-centric models and approaches. But just how realistic is this? Today’s enterprises are sometimes criticized for legacy thinking, but they also have to bear the burden of infrastructure, supply chains, security, governance frameworks, and customer drivers that can’t be changed at the flick of the switch. Here's the good news: enterprise concerns and cloud native principles may be better aligned than you think. Flexible infrastructure is becoming a staple of modern data centers. Microservices models align with software architecture best practices, while Agile innovation approaches such as DevOps are maturing in governance. At the same time, cybersecurity and risk management is an area of traditional strength, and operational principles used in the enterprise are as important as ever. Find out how businesses can adopt cloud native principles without throwing away what it means to be an enterprise. Recorded on May 2, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Cloud , you’ll learn about the complex, ever-evolving world of the cloud. You’ll discover how successful companies have adopted and embraced this massive network of shared information and how you can follow their lead to transform your organization and prepare for the Next Economy.
    Anmerkung: Online resource; Title from title screen (viewed September 17, 2019)
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  • 32
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    Test in production : Yes, you can (and you should) (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 32 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Testing in production has gotten a bad rap. People act like testing in production implies you aren’t doing due diligence with your tests before production. But it’s more like a fact of life: you can only catch the easy bugs in staging—the known-unknowns, the things you predicted would fail, and the things that have failed before. Which isn’t nothing, but it’s no better than running tests on your laptop. Most interesting problems are only going to manifest under real workloads, on real data, with real users doing unpredictable things under real concurrency and resource pressure. So you should use much fewer of your scarce engineering cycles poring over staging and much more of them building guard rails for prod. Production—where your customers live—is the only environment that matters. Time spent interacting with nonprod systems is wasted time. Replicas are not valuable for helping build your instincts, your skill set, your intuition. Secondary environments actually train you to expect faulty assumptions and to take dangerous shortcuts and run terrifying commands. You should force people to develop and test on production as much as possible and interact with production every day. Charity Majors (Honeycomb) dives into tooling and shares ways to harden production and make it safe for engineers to do their work directly on it—from deploys and canarying to feature flags, instrumentation and observability, human practices and workflows, and much more. This session was recorded at the 2019 O'Reilly Velocity Conference in San Jose.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 33
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    Using AutoML to automate selection of machine learning models and hyperparameters (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 43 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. In recent years, AutoML has fostered a fundamental shift in how organizations approach machine learning, making it more accessible to both experts and nonexperts. Most real-world data science projects are time-consuming, resource intensive, and challenging. Besides data preparation, data cleaning, and feature engineering, data scientists often spend a significant amount of time on model selection and tuning of hyperparameters. Automated machine learning changes that, making it easier to build and use machine learning models in the real world. Francesca Lazzeri and Wee Hyong Tok (Microsoft) lead a gentle introduction to how AutoML works and the state-of-art AutoML capabilities that are available. You’ll learn how to use AutoML to automate selection of machine learning models and automate tuning of hyperparameters. Topics include: An introduction to AutoML How AutoML works The libraries and cloud services that support AutoML An energy demand forecasting use case How to get started with automated machine learning This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 34
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    Unblocked (Audio Book) (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 8 hr., 37 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local ; Audiobooks ; local
    Kurzfassung: Imagine it's 1994 and the dawn of the internet. In many ways, it is. A growing army of pioneers is once again laying the rails for a new digital world. Like the first digital revolution, this one will also transform the way we live, work and play. It will disrupt entrenched industries. And shatter conventional business models. With so much at stake, how do you prepare? Unblocked takes you past the hype and insider speak to show you what blockchains are and how they’re poised to change our world. Blockchains are more than flexible multi-tools that hold promise to solve our most pressing digital problems including privacy risks, hacks, data misuse, fakes and fraud. Along with a family of related technologies, they are a social movement that offers an abundance of opportunity for those who know how to leverage them. Unblocked explains: Why ignoring this technology exposes you to competitive disruption What's coming in this next era—no technical background required How to prepare your organization to respond to the coming shift
    Anmerkung: Online resource; Title from title screen (viewed July 18, 2019)
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  • 35
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    On microservices, bounded contexts, and everything in between (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 43 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: “Ninety-five percent of the words are spent extolling the benefits of ‘modularity’ and that little, if anything, is said about how to achieve it”—Glenford J. Myers, 1978. The above quote is 40 years old. Today, four decades later, nothing has changed except terminology. Time to fix this. Vladik Khononov (Invesus Group) explains how to decompose a system into loosely coupled components: how to draw boundaries between services, how to decide whether some logic belongs to one service or another, and how domain-driven design can help us make those decisions. Finally, he takes a stab at answering the age-old question of what part of a microservice should be “micro” and how it can be measured. You’ll hear about neither Docker nor Kubernetes. Actually, nothing related to infrastructure. Instead, you’ll dive into the difference between microservices and bounded contexts, discover when each pattern should be used, and get takeaways from Vladik’s experience optimizing microservices-quotebased architectures at Naxex. This session was recorded at the 2019 O'Reilly Software Architecture Conference in San Jose.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 36
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    Observability for developers : How to get from here to there (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 33 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: There’s been a lot of talk about software ownership—but what does “owning code in production” really mean for developers day to day? Many development teams still reach for logs in production as the most familiar way to bridge the development environment with production. Christine Yen (Honeycomb) makes the case that observability—and the skills to craft the right graphs and read them—benefits developers more than it does operators by examining several instances where well-informed devs can supercharge their development process, such as data-driven product decisions (or how to know more about what needs to be done than your PM), rewrites and migrations, feature flags and testing in production, and fine-grained performance analysis. Then, she lays out a series of steps to get your team from grepping unstructured text logs to outputting and analyzing well-structured traces. Instrumentation and observability aren’t all-or-nothing endeavors, and you’ll leave with an idea of the next step you can take to improve your ability to understand your production systems. This session was recorded at the 2019 O'Reilly Velocity Conference in San Jose.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 37
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    Spotlight on Learning from Failure : Stablecoin Derisks The Blockchain Landscape with Wayne Chang (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 55 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Stablecoins—cryptocurrencies designed to avoid volatility—have been tried various times in the past with little to no success. Today, they're seeing a resurgence, as multiple firms work to develop their own stablecoins in an effort to solve the volatility crisis in cryptocurrency. Although nascent, these deployments have seen a wide amount of traction with both trading and application. But does the increased interest in stablecoins actually assuage the volatility crisis, and what does it mean for the future of blockchain-enabled businesses? In this edition of Spotlight on Learning from Failure , find out how stablecoins work, what opportunities and risks they present, and what to expect in the future with regard to the greater blockchain landscape. Wayne Chang will explain the history of stablecoins, how they solve for volatility issues in businesses and applications, and how they’ve been implemented over time. Wayne will also discuss the trade-offs between different stablecoins and how they fit into existing business models in order to drive value. Recorded on May 28, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Learning from Failure , you’ll discover the lessons learned from failures both large and small. You’ll discover how successful companies have addressed setbacks, missteps, and challenges and how you can grow from their examples.
    Anmerkung: Online resource; Title from title screen (viewed October 4, 2019)
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  • 38
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    Spotlight on Data : Improving Uber’s Customer Support with Natural Language Processing and Deep Learning with Piero Molino (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 57 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: For a company looking to provide delightful user experiences, it's critical to resolve customer issues quickly and efficiently. Uber has implemented COTA, a system that helps representatives provide the best experience to customers by suggesting the best solutions. COTA improves the speed and reliability of customer support through automated ticket classification and answer selection for support representatives. By improving speed and reliability, COTA also helps reduce customer support operations costs. In this Spotlight on Data , find out how Uber leverages large-scale data and deep learning models for operational efficiency and improved user experience. Piero Molino details how Uber has reduced issue resolution time by 20% while maintaining levels of customer satisfaction, using NLP, deep learning, A/B testing, and productionized models. Recorded on July 2, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Data , you’ll learn about, discuss, and debate the tools, techniques, questions, and quandaries in the world of data. You’ll discover how successful companies leverage data effectively and how you can follow their lead to transform your organization and prepare for the Next Economy.
    Anmerkung: Online resource; Title from title screen (viewed October 17, 2019)
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  • 39
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    Spotlight on Cloud : The Hidden Costs of Kubernetes with Bridget Lane (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 1 hr., 0 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: If you’re considering Kubernetes, you’ve probably thought about the potential time and cost savings you could stand to gain. At Gannett and USA TODAY, migrating to Kubernetes cut their daily infrastructure spend in half and reduced their network failover time from almost four hours to about 10 minutes. These are huge savings for any company. That said, there were a number of costs to migrating that they didn't anticipate. Gannett’s migration to Kubernetes had a ripple effect through its entire stack—from its CI pipelines to its approach to monitoring and even employee happiness. In this case study, Bridget Lane, Gannett’s manager of developer solutions, details the problems her team faced while migrating and explain how they dealt with them. Recorded on September 19, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Cloud , you’ll learn about the complex, ever-evolving world of the cloud. You’ll discover how successful companies have adopted and embraced this massive network of shared information and how you can follow their lead to transform your organization and prepare for the Next Economy.
    Anmerkung: Online resource; Title from title screen (viewed October 4, 2019)
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  • 40
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    Spotlight on Cloud : Reducing the Impact of Service Outages with Generic Mitigations with Jennifer Mace (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 59 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: While a mitigation is any action you might take to reduce the impact of a breakage—such as SSHing into an instance and clearing the cache or switching off machines to close down a vulnerability—generic mitigations are actions that first responders can take even before the root cause is fully understood. As such, they’re useful for addressing a wide variety of outages. Every service should employ at least one or two generic mitigations to minimize outage impacts. In this Spotlight on Cloud , Jennifer Mace, site reliability engineer at Google, shows you how to distinguish between specific and generic mitigations and how to identify what generic mitigations your service might need. Recorded on September 25, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Cloud , you’ll learn about the complex, ever-evolving world of the cloud. You’ll discover how successful companies have adopted and embraced this massive network of shared information and how you can follow their lead to transform your organization and prepare for the Next Economy.
    Anmerkung: Online resource; Title from title screen (viewed October 21, 2019)
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  • 41
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    Shortcuts that short-circuit talent pipelines : Data-driven optimization of hiring (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 43 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: As acute shortages for data scientists and engineers further develop in competitive talent markets, it’s imperative to address biases and bottlenecks in the hiring of data scientists and engineers. Most companies approach hiring and talent management as an art, relying on judgment and experience when conceptualizing jobs, drafting JDs, and screening and assessing candidates. Maryam Jahanshahi (TapRecruit) explains how often-innocuous recruiting decisions have dramatic impacts on hiring outcomes, from the success of a hiring process to the candidate pool composition and the amount of time it takes to hire. She then discusses how using data-driven approaches provide an arbitrage opportunity that has significant impact on both the quality and diversity of candidate pools for data science and engineering roles. Maryam covers the role of confounders in the hiring process, explores the complexity of job descriptions, and shares how she extracted signal from noise to extract the key conclusions she presents. She then presents key hiring heuristics in three vignettes: the one word that creates significant ripple effects at every stage of the hiring process; the surprising way that startups do better in the talent war for data scientists and engineers; and hacking diversity, or how she and her team learned to reproducibly and robustly recruit a diverse pool of data scientists and engineers. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 42
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    From flat files to deconstructed databases : The evolution and future of the big data ecosystem (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 44 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Over the past 10 years, big data infrastructure has evolved from flat files in a distributed filesystem to an efficient ecosystem to a fully deconstructed and open source database with reusable components. With Hadoop, we started from a system that was good at looking for a needle in a haystack using snowplows. We had a lot of horsepower and scalability but lacked the subtlety and efficiency of relational databases. But since Hadoop provided the ultimate flexibility compared to the more constrained and rigid RDBMSs, we didn’t mind and plowed through. However, machine learning, recommendations, matching, abuse detection, and data-driven products in general require a more flexible infrastructure. Over time, we started applying everything that had been known to the database world for decades to this new environment. We’d been told loud enough how Hadoop was a huge step backward. And it was true to some degree. The key difference was the flexibility of the Hadoop stack. There are many highly integrated components in a relational database and decoupling them took some time. Today, we see the emergence of key components, such as optimizers, columnar storage, in-memory representation, table abstraction, and batch and streaming execution, as standards that provide the glue between the options available to process, analyze, and learn from our data. We’ve been deconstructing the tightly integrated relational database into flexible reusable open source components. Storage, compute, multitenancy, and batch or streaming execution are all decoupled and can be modified independently to fit every use case. Julien Le Dem (WeWork) discusses the key open source components of the big data ecosystem—including Apache Calcite, Parquet, Arrow, Avro, and Kafka as well as batch and streaming systems—and explains how they relate to each other and how they make the ecosystem more of a database and less of a filesystem. (Parquet is the columnar data layout to optimize data at rest for querying. Arrow is the in-memory representation for maximum throughput execution and overhead-free data exchange. Calcite is the optimizer to make the most of our infrastructure capabilities.) Julien also explores the emerging components that are still missing or haven’t become standard yet to fully materialize the transformation to an extremely flexible database that lets you innovate with your data. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 43
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    Fraud detection without feature engineering (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 40 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: In many industrial ML applications, feature engineering consumes the lion’s share of time, energy, and resources. Deep learning promises to replace feature engineering with models that learn end to end from “close-to-reality” data and has convincingly realized this promise for computer vision and natural language processing. But does this promise apply outside of these domains? Pamela Vagata (Stripe) explains how Stripe has applied deep learning techniques to predict fraud from raw behavioral data. Since fraud detection is a critical business problem for Stripe, the company already had a well-tuned feature-engineered model for comparison. Stripe found that the deep learning model outperforms the feature-engineered model both on predictive performance and in the effort spent on data engineering, model construction, tuning, and maintenance. Join in to discover how common industry practice could shift toward deeper models trained end to end and away from labor-intensive feature engineering. This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 44
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    Expert Playlist Intro : Lessons from Amazon, Google, Apple and Other Silicon Valley Giants (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 4 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: In this brief introduction to Tim O'Reilly's Expert Playlist , find out why he selected these pieces to study how some of the most advanced companies on the planet not only use technology but build their culture to support it.
    Anmerkung: Online resource; Title from title screen (viewed August 25, 2019)
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  • 45
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    Disrupting data discovery (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 42 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Before any analysis can begin, a data scientist needs to discover the right data sources to analyze, understand them, and determine whether they can trust them. Unfortunately, data discovery is very inefficient today. Countless hours are lost trying to find the right data to use. (The most common way still remains to ask a coworker.) Gaining trust in data requires running a bunch of queries (max timestamp, counts per day, count distincts, etc.) that waste time and add unnecessary load on the databases. There’s no clear way to know how to find folks to answer questions about the table. And worst of all, many times analysis is redone and models are rebuilt because previous work isn’t discoverable. Lyft has reduced the time it takes to discover data by 10x by building its own data portal, Amundsen. Amundsen is built on three key pillars: an augmented data graph, an intuitive user experience, and centralized metadata. Amundsen uses a graph database under the hood to store relationships between various data assets (tables, dashboards, protobuf events, etc.). What’s unique to Amundsen is that it treats people as a first-class data asset; in other words, there’s a graph node for each person in the organization that connects to other nodes (like tables, and dashboards). In addition, Amundsen runs PageRank using data from access logs to power search ranking, similar to how Google ranks web pages on the internet. Finally, Amundsen gathers metadata from various different sources (Hive, Presto, Airflow, etc.) and exposes it in one central place. The right place to store all this metadata is a work in progress. Mark Grover and Tao Feng (Lyft) offer a demo of Amundsen and lead a deep dive into its architecture, covering how it leverages centralized metadata, page rank, and a comprehensive data graph to achieve its goal. They also explore the future roadmap, unsolved problems, and its collaboration model. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 46
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    Distributed event-driven services : From the trenches (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 50 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: In his best-selling book Patterns of Enterprise Application Architecture , Martin Fowler famously coined the first law of distributed computing—"Don’t distribute your objects"—implying that working with this style of architecture can be challenging. A few years ago, Barclays embarked on a journey to migrate its legacy services with the objective of achieving a high level of scale, resilience, and reliability, mainly employing an ecosystem of focused, distributed services. It’s fair to say that the company has discovered through firsthand experience that there’s quite a bit of truth to that statement. Prem Chandrasekaran provides an insider scoop on Barclays’s journey deploying services to a private cloud-based infrastructure making use of foundational patterns such as domain-driven design, event-driven architecture (EDA), command query responsibility segregation (CQRS), and event sourcing, among others. Prem recounts some of the challenges faced during the transformation and sheds light on the things that worked well and those that didn’t. Topics include: How to establish buy-in with senior stakeholders and management How to recruit, onboard, and train new team members How to establish the boundaries of your teams and services How to focus on the right amount and kinds of tests in your pipelines How to ensure database and API compatibility How to centralize and externalize secrets and configuration How to diagnose and pinpoint issues in unattended, asynchronous processes How to decide between blue-green and rolling deployments How to work with private and public cloud implementations This session was recorded at the 2019 O'Reilly Software Architecture Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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    Deep learning for recommender systems, or how to compare pears with apples (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 42 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance. Join Marcel Kurovski (inovex) to explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking. The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP5. It also scales well for millions of items and users returning recommendations in tens of milliseconds. This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
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    Spotlight on Learning from Failure : Assessing Talent Beyond Technical Skills with Tony Tjan (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 57 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: One of the most difficult business challenges has nothing to do with data or technology—it has to do with people. While it’s fairly easy to assess potential hires’ knowledge and skills, it’s much harder to gauge soft traits like attitude. How can you ensure your first impressions are accurate so you can avoid costly hiring mistakes, and correctly identify rising stars and make the most of their talent? Tony Tjan explains how overlooking personality and attitude in favor of technical expertise can cost you. Make sure you’re never in that situation by learning the most important questions to ask during the hiring process to build a stronger, more successful organization. Recorded on March 26, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Learning from Failure , you’ll discover the lessons learned from failures both large and small. You’ll discover how successful companies have addressed setbacks, missteps, and challenges and how you can grow from their examples.
    Anmerkung: Online resource; Title from title screen (viewed August 27, 2019)
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    Spotlight on Cloud : The Future of Internet Security with Bruce Schneier (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 58 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Long gone are the days when computer security was about data. Now, it’s about the much more critical and personal issues of life and property. The impact of this change means two things: Data authentication and integrity concerns will trump those of confidentiality The idea of an internet without regulations will be a thing of the past While these consequences are inevitable, we can prepare for them. First, by looking at previous attempts to secure these systems. Then, by considering the appropriate technologies, laws, regulations, economic incentives, and social norms we should focus on going forward. Recorded on April 4, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Cloud , you’ll learn about the complex, ever-evolving world of the cloud. You’ll discover how successful companies have adopted and embraced this massive network of shared information and how you can follow their lead to transform your organization and prepare for the Next Economy.
    Anmerkung: Online resource; Title from title screen (viewed September 17, 2019)
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    O'Reilly Velocity Conference 2019 - San Jose, California (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 61 hr., 11 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: O’Reilly Media’s Velocity San Jose 2019 conference had one overarching goal: offer conference attendees the opportunity to gather expert insight on building and maintaining cloud native systems. How did Velocity meet that goal? By giving the world’s best cloud native system engineers, SREs, and DevOps experts the chance to share their experiences. For example: Alex Chen (Alibaba Cloud) described how cloud native storage products such as table storage, object storage, and log storage enabled the Alibaba platform to handle RMB 10B worth of sales in two minutes and five seconds. Ory Segal's (PureSec) revealed how attackers approach and exploit the weaknesses of serverless and what you can do about it. Nivia Henry (Spotify) demonstrated how an algorithm is built and what you can do to catch, reduce, or eliminate the algorithmic biases that produce infamous events like Tay the racist Twitter Bot. This video compilation contains all of these talks plus the best keynotes, tutorials, and tech sessions delivered at Velocity San Jose 2019. Looking to build your knowledge of how to build and maintain cloud native systems? Get this compilation and you’ll be on your way. Highlights include: Complete video recordings of Velocity San Jose 2019’s best tutorials, keynote speeches, and technical sessions—contains hours of material to review and study at your own pace and schedule. Cloud engineering tutorials, such as Patrick Meenan (Facebook) on fixing broken HTTP/2 deployments; Michael Kehoe (LinkedIn) on using Terraform to modernize your cloud infrastructure; and Ben Bleything (Google) on using the open source InSpec framework to build infrastructure and compliance tests. Keynotes from luminaries such as Liz Fong-Jones (Honeycomb), Everett Harper (Truss), Julie Horvath (Apple), Alex Qin (Code Cooperative), Lena Hall (Microsoft), and Jessica Kerr (Atomist). Sessions devoted to building secure and resilient systems, including Ian Coldwater’s (Independent) deep dive into Kubernetes CVE-2018-1002105 protections and Nora Jones (Slack) on the advanced approaches required for each piece of the chaos experiments cycle. Special sessions called “Overcoming Obstacles: Lessons in Resilience,” featuring the stories of cloud engineering success in the face of overwhelming barriers from practitioners such as Ryan Kitchens (Netflix), Tim Bonci (Vistaprint), and Tammy Butow (Gremlin). Sessions covering production engineering, SRE, and DevOps, including Charity Majors (Honeyco...
    Anmerkung: Online resource; Title from title screen (viewed June 12, 2019)
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    Strata Data Conference 2019 - San Francisco, California (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 122 hr., 4 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Thousands of the data scientists, analysts, engineers, developers, and executives converged at the Strata Data Conference San Francisco in March 2019 to absorb the insights and wisdom of the data world's best minds. The conference featured more than 300 speakers, 10 keynotes, 10 tutorials, and 150+ technical sessions. This video compilation captures the best from the conference, offering more than 100 hours of material to review at your own pace. Highlights include: The Strata Business Summit - speakers, executive briefings, and tech sessions laser focused on a central theme: How do the world’s leading companies build their successful data strategies? Learn about recommendation engines, AI-based personalization solutions, data governance, ML based customer insight harvesting, and more from data wizards like Zachery Anderson (Electronic Arts), Eric Bradlow (The Wharton School), David Talby (Pacific AI), Paco Nathan (derwen.ai), Jonathan Francis (Starbucks), and JoLynn Lavin (General Mills). The Strata Data Ethics Summit – Is your AI really making good decisions or have you built a deceptive black box that reinforces ugly stereotypes? Alistair Croll (Strata Chair), Tim O'Reilly (O'Reilly Media), and Susan Etlinger's (Altimeter Group) eight hour deep dive into the thorny issues of data and algorithms with help from Jana Eggers (Nara Logics), Rumman Chowdhury (Accenture), Kathy Baxter (Salesforce), Carole Piovesan (McCarthy Tétrault), and more. Hours of tutorials from the world's top data engineers, such as Francesca Lazzeri (Microsoft) and Holden Karau (Google) on training and deploying models with Kubeflow across different cloud vendors; Dean Wampler (Lightbend) on performing machine learning using Kafka-based streaming pipelines; and Jason Dai (Intel) on the Analytics Zoo, an analytics/AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline. Sessions devoted to Data Science, Machine Learning & AI, including Sharad Goel (Stanford University) on the challenges of "fair machine learning", which aims to ensure that decisions guided by algorithms are equitable; Kelley Rivoire (Stripe) on scaling machine learning using the Railyard API; Vinod Vaikuntanathan (MIT) on performing machine learning on encrypted data; and Jeremy Howard (platform.ai) on recent advances in deep learning that allow non-engineers to train neural networks from scratch without needing code or pre-existing labels. Sessions focus...
    Anmerkung: Online resource; Title from title screen (viewed March 27, 2019)
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    Spotlight on Data : Data as an Asset with Friederike Schüür and Jen van der Meer (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 60 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Our online lives have long left data traces, and the adoption of smartphones, wearables, smart speakers, smart doorbells, and smart everything else has expanded the range of data-collection devices. The early practice of data science on exhaust data—data generated as the by-product of operations—primed the operational use of data. But we can now craft data assets that move beyond exhaust data to unlock an entire suite of new opportunities—new business models, new value propositions, and new methods of strengthening customer relationships. In this edition of Spotlight on Data , Friederike Schüür and Jen van der Meer present an approach to data strategy that goes beyond the mere operational use of data. You'll learn how our current thinking about data strategy—about everything from technology to people and process recommendations—must be reconfigured to move toward data as an asset: a strategy that accelerates and compounds and a set of smart choices that unlocks more value over time. Recorded on May 20, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Data , you’ll learn about, discuss, and debate the tools, techniques, questions, and quandaries in the world of data. You’ll discover how successful companies leverage data effectively and how you can follow their lead to transform your organization and prepare for the Next Economy.
    Anmerkung: Online resource; Title from title screen (viewed October 4, 2019)
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    Turn devices into data scientists—at the edge (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 36 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Today’s approach to processing streaming data is based on legacy big-data centric architectures, the cloud, and the assumption that organizations have access to data scientists to make sense of it all—leaving organizations increasingly overwhelmed. Simon Crosby (SWIM.AI) shares a new architecture for edge intelligence that turns this thinking on its head. Edge intelligence (encompassing analytics, learning and prediction, and edge computing) can frequently be accomplished on the fly on streaming data, cheaply, at the edge, without data scientists. Simon demonstrates how you can save up to $5,000 a month in cloud processing and storage costs while delivering accurate predictions that can transform outcomes, using well-established architectural pillars, such as the distributed actor model, to process voluminous real-time data at the edge, along with the rich commons of open source analytics and learning tools like Flink and Spark, on nothing more than a $200 device such as an NVIDIA Jetson. The key insight is to use streaming data to build a digital twin model on the fly at the edge, avoiding a ton of complexity and infrastructure costs. Instead, a user defines the entities in their environment (e.g., traffic intersections, compressors, or assembly robots) that deliver data. Using the stateful distributed actor model, you can dynamically build a digital twin (actor) model of the real-world from the data, linking twins based on their relationships. Each digital twin reduces, labels, and analyzes its data and self-trains a machine learning model to predict future performance, at the edge, discarding the original data. This method needs only a tiny fraction of the resources of a big data solution and delivers results in real time. As a result, it bypasses the dev, ops, and data science challenges of edge intelligence, effectively turning devices into data scientists—or at least, building data science twins for entities in the real world. This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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    The Elements of Kubernetes : Foundational concepts for apps running on Kubernetes (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 45 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: The Elements of Style is a foundational guide to writing well. It effectively summarizes how to harness the power of the English language to write high-quality prose of almost any kind. In computing, we have similar guides for various technologies: Python offers The Zen Of Python , Ruby has The Rails Doctrine , and so on. These documents are a North Star that guides their community toward the same goals. We need a similar guide for Kubernetes. It should set best practices for how app developers and operators should think about and use the features in Kubernetes to build and deploy reliable, stable apps. We don’t have one today, but the Kubernetes community has the experience to build one. Drawing on his own and the community’s experiences, Aaron Schlesinger (Microsoft) details an “Elements of Kubernetes” guide, to be used by app developers and operators alike. You’ll leave with a list of best practices you can apply to your app now. Topics include: Setting up your new dev-test workflow Building a good CI/CD pipeline Debugging your app in production Breaking up your monolithic app into microservices (and when not to do it) Building crash-only software This session was recorded at the 2019 O'Reilly Software Architecture Conference in New York.
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    Technology Strategy Patterns (Audio Book) (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 9 hr., 19 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local ; Audiobooks ; local
    Kurzfassung: Technologists who want their ideas heard, understood, and funded are often told to speak the language of business—without really knowing what that is. This book’s toolkit provides architects, product managers, technology managers, and executives with a shared language—in the form of repeatable, practical patterns and templates—to produce great technology strategies. Author Eben Hewitt developed 39 patterns over the course of a decade in his work as CTO, CIO, and chief architect for several global tech companies. With these proven tools, you can define, create, elaborate, refine, and communicate your architecture goals, plans, and approach in a way that executives can readily understand, approve, and execute. This book covers: Architecture and strategy: Adopt a strategic architectural mindset to make a meaningful material impact Creating your strategy: Define the components of your technology strategy using proven patterns Communicating the strategy: Convey your technology strategy in a compelling way to a variety of audiences Bringing it all together: Employ patterns individually or in clusters for specific problems; use the complete framework for a comprehensive strategy
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    Manipulating and measuring model interpretability (2019)
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 40 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Machine learning is increasingly used to make decisions that affect people’s lives in critical domains like criminal justice, fair lending, and medicine. While most of the research in machine learning focuses on improving the performance of models on held-out datasets, this is seldom enough to convince end users that these models are trustworthy and reliable in the wild. To address this problem, a new line of research has emerged that focuses on developing interpretable machine learning methods and helping end users make informed decisions. Despite the growing body of work in developing interpretable models, there is still no consensus on the definition and quantification of interpretability. Forough Poursabzi-Sangdeh (Microsoft Research NYC) argues that to understand interpretability, we need to bring humans in the loop and run human-subject experiments. Forough approaches the problem of interpretability from an interdisciplinary perspective built on decades of research in psychology, cognitive science, and social science to understand human behavior and trust. She describes a set of controlled user experiments in which researchers manipulated various design factors in models that are commonly thought to make them more or less interpretable and measured their influence on users’ behavior. The findings emphasize the importance of studying how models are presented to people and empirically verifying that interpretable models achieve their intended effects on end users. This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
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    Kubernetes APIs under the hood (2019)
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 40 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Kubernetes provides a powerful set of APIs and abstractions for building distributed systems. Additionally, it provides users with the ability to build and install their own extension APIs alongside the core APIs. Phillip Wittrock (Google) covers how core Kubernetes APIs are designed and built and teaches the basics of writing an installable custom Kubernetes API. Phillip provides an introduction to fundamental API concepts, including resources, controllers, and reconciliation loops; then, he provides an end-to-end walkthrough, answering what happens after running “kubectl apply.” He discusses Kubernetes resource versioning, finalizers, defaulting, and validation and asynchronous and level-based controller architecture. This session was recorded at the 2019 O'Reilly Velocity Conference in San Jose.
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    Having the bubble : How your experts build, maintain, and spread deep system knowledge (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 34 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: We fret about how to break system knowledge out of knowledge silos—the expert individuals with a deep intuitive understanding of our complex systems. How do these people engage with the system and act to protect, fortify, and improve it? How can engineering organizations protect and fortify experts as a key part of their observability strategy? Beth Long (New Relic) explains how those experts represent both a vulnerability and a strength and why understanding them as a key mechanism in your larger systems helps you harness their power and protect against fragility. This session was recorded at the 2019 O'Reilly Velocity Conference in San Jose.
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  • 59
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    Game engines and machine learning (2019)
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 42 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Games are wonderful contained problem spaces, making them great places to explore AI—even if you’re not a game developer. Paris Buttfield-Addison (Secret Lab Pty. Ltd.), Mars Geldard (University of Tasmania), and Tim Nugent (lonely.coffee) teach you how to use Unity to train, explore, and manipulate intelligent agents that learn. You’ll train a quadruped to walk, then train it to explore, fetch, and manipulate the world. It’s a little bit technical, a little bit creative. Join Paris, Mars, and Tim to learn how to use game technologies such as Unity to further your understanding of machine learning fundamentals and solve problems. Topics include: How video game engines are a perfect environment to constrain a problem and train an agent How easy it is to get started, using Unity How to build up a model and use it in the engine to explore a particular idea or problem This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
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    Everything is a little bit broken; or, The illusion of control (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 38 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: We never change the amount of work or technical debt; we just shift it, and with it, we change how it emerges and appears. Our systems don’t have to be perfect to be operational—planes, networks, and elite athletes all function at extremely high levels even though they are not operating at 100%. As an industry, we have moved the locus of control from hardware to operating system to virtual machine to container to orchestration, and now we’re approaching serverless. None of that has reduced the amount of work you have to do; it just makes it so you can reuse and conceptually compress the work of others. Since we’re making the work in our tools less visible, we also have less control over how they work. We assume the promises that have been true will continue to be true, but that isn’t in our control. Heidi Waterhouse (LaunchDarkly) explains how to handle this level of uncertainty. You’ll learn to add in error budgets, layered access, and other accommodations for failure and to design your systems for function over form or purity. You’ll leave with some concrete ideas about how to add resiliency to your system by learning to trust but mitigate your reliance on perfect performance of your underlying tools. This session was recorded at the 2019 O'Reilly Velocity Conference in San Jose.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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    Deep learning for time series data (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 42 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Arun Kejariwal (Independent) and Ira Cohen (Anodot) share a novel two-step approach for building more reliable prediction models by integrating anomalies in them. The first step uses anomaly detection algorithms to discover anomalies in a time series in the training data. In the second, multiple prediction models, including time series models and deep networks, are trained, enriching the training data with the information about the anomalies discovered in the first step. Anomaly detection for individual time series is a necessary but insufficient step due to the fact that anomaly detection over a set of live data streams may result in anomaly fatigue, thereby limiting effective decision making. One way to address the above is to carry out anomaly detection in a multidimensional space. However, this is typically very expensive computationally and hence not suitable for live data streams. Another approach is to carry out anomaly detection on individual data streams and then leverage correlation analysis to minimize false positives, which in turn helps in surfacing actionable insights faster. They then walk you through marrying correlation analysis with anomaly detection, discuss how the topics are intertwined, and detail the challenges you may encounter based on production data. They also showcase how deep learning can be leveraged to learn nonlinear correlation, which in turn can be used to further contain the false positive rate of an anomaly detection system. This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
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    Spotlight on Learning from Failure : Data Ethics Is Good Business with Heather Krause (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 58 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Ethical lapses aren’t just embarrassing and expensive—they can put your organization at serious risk in a number of ways and damage its reputation and bottom line. If you collect any kind of data, you’re more vulnerable to ethical errors than you may realize. From acquisition and model-building to algorithms, it’s critical to view all aspects of data science through an ethical lens and implement best practices to ensure accuracy. We need only look to some very public failures as proof. Learn about three cases when data ethics mistakes cost companies both money and trust—and how your business can avoid their mistakes. Recorded on January 29, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Learning from Failure , you’ll discover the lessons learned from failures both large and small. You’ll discover how successful companies have addressed setbacks, missteps, and challenges and how you can grow from their examples.
    Anmerkung: Online resource; Title from title screen (viewed August 27, 2019)
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  • 63
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    Spotlight on Learning from Failure : Creating Better Data Pipelines with Natalino Busa (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 59 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: When considering the complexities of implementing a next-generation data pipeline, the risk of over-promising and under-delivering is incredibly high due to the overwhelming expectations placed on predictive analytics today. It’s critical to look at a number of factors—like your company’s culture and the historic promise gap between ETL (extract, transform, load) in the 1970s and AI (artificial intelligence) in the 2010s—to overcome the challenges and succeed. Natalino Busa discusses how to bust the myths, deconstruct the false assumptions, and avoid common pitfalls in order to build and deliver successful data pipelines that deliver real value to your organization. Recorded on February 26, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Learning from Failure , you’ll discover the lessons learned from failures both large and small. You’ll discover how successful companies have addressed setbacks, missteps, and challenges and how you can grow from their examples.
    Anmerkung: Online resource; Title from title screen (viewed August 27, 2019)
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  • 64
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    Spotlight on Data : Creating Smart Products Requires Collaboration with Gretchen Anderson (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 55 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: We're experiencing a boom in smart products and services, from virtual assistants like Alexa to robotic surgical tools. But as we design and develop products of increasing complexity—incorporating machine learning to drive autonomous decision making—measuring output and outcomes also explodes in complexity. In the face of all this complexity, how do you design your teams and collaborative processes to ensure a successful product? Teams have to move away from merely seeking “approvals” and toward collaborating on the goals and parameters; they must also target use cases that will make or break the experience for end users. The more dynamic the technology, the wider the range of diverse perspectives that must inform the process. Data scientists, designers, developers, lawyers, and regulators all need to weigh in, and their voices need to be heard and accounted for. Recorded on April 15, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Data , you’ll learn about, discuss, and debate the tools, techniques, questions, and quandaries in the world of data. You’ll discover how successful companies leverage data effectively and how you can follow their lead to transform your organization and prepare for the Next Economy.
    Anmerkung: Online resource; Title from title screen (viewed September 17, 2019)
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  • 65
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    Applied machine learning in finance - 2019 O'Reilly Strata Data Conference, San Francisco (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 34 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed by both sell-side and buy-side institutions. Techniques like time series analysis, stochastic calculus, multivariate statistics, and numerical optimization are often used by “quants” for modeling asset prices, portfolio construction and optimization, and building automated trading strategies. Chakri Cherukuri (Bloomberg LP) explains how machine learning and deep learning techniques are being used in quantitative finance. Chakri outlines use cases for machine learning in finance and dives into a few examples, involving both structured and unstructured datasets, to examine in detail how machine learning models can be used for predictive analytics. Chakri details how these models work under the hood and explores the interpretability of these models. Along the way, you’ll look at novel interactive visualizations and diagnostic plots that will help you better understand these models.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 66
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    The well-rounded architect (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 48 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Being a successful architect requires more than just a good understanding of architecture. Patrick Kua (N26) explores the breadth of skills and experience an architect should focus on and outlines the balance of traits that makes a well-rounded architect. Patrick examines the architect role from a number of perspectives—looking both inward and outward—to determine the different skill areas an architect requires to be successful. Patrick also shares some of the typical traps that an architect who focuses too much on a single aspect falls into and discusses their negative consequences to teams and organizations. Patrick concludes by offering a map for you to assess and plan your own development to learn and maintain a balanced set of skills and become a well-rounded architect. This session was recorded at the 2019 O'Reilly Software Architecture Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 67
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    The ethics of analytics (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 59 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: As more and more data is being collected, concerns are constantly being raised about what data is appropriate to collect and how (or if) it should be analyzed. There are many ethical, privacy, and legal issues to consider, and no clear standards exist in many cases as to what is fair and what is foul. This means that organizations must consider their own principles and risk tolerance in order to implement the right policies. Bill Franks (International Institute For Analytics) explores a range of ethical, privacy, and legal issues that surround analytics today, framing the big questions to consider and detailing some of the trade-offs and ambiguities that must be addressed to answer them. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 68
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    Database migrations don't have to be painful, but the road will be bumpy (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 31 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Change is the only constant; upgrades are inevitable. Believe it or not, this also applies to your database. There are many drivers for such changes, such as technology stack updates and new data center or event cloud migrations. A database migration isn’t an easy task. Preparation and execution takes time and leaves no room for mistakes. And once you start, there’s no going back. But is this the only way? Adrian Lungu and Serban Teodorescu (Adobe) explain how—inspired by the green-blue deployment technique—the Adobe Audience Manager team developed an active-passive database migration procedure that allows them to test database clusters in production, minimizing the risks without compromising the innovation. It was successfully applied twice to upgrade the entire database technology stack—but it’s never a smooth move when your databases are 200-node Cassandra clusters with hundreds of terabytes of data and downtime is not an option. The first migration was focused only on software upgrades. For the second upgrade, the team’s confidence was so high that they added a twist: a couple of more changes besides the Cassandra version—AWS instance type, operating system, disk settings, memory settings, JVM, and a few more. What could go wrong? Well. . .everything. Adrian and Serban describe the migration technique and present an extensible database client that makes all the active-passive management possible with just a configuration change. Yes, you heard it right. No code changes, just configurations. They then share a series of tales and lessons learned during the migration of over 500 Cassandra nodes. Most of the lessons aren’t Cassandra related but instead apply to hardware, drivers, operating system, or the JVM—debugging for days and searching for that metric anomaly or that log line that would give us a hint on what went wrong. Join in to see how you can avoid some of these pitfalls in your own projects. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 69
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    Choreographing microservices (San Jose, CA) (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 1 hr., 26 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Choreographed microservices talk to each other asynchronously, blindly broadcasting notifications into a service cloud. Those notifications are handled by whatever client services are interested. These systems eliminate many of the problems associated with orchestrated systems (which work more like synchronous function calls) and are typically much faster than orchestrated systems, but they have their own idiosyncrasies and implementation challenges. Allen Holub explores the inherent problems in orchestrated systems and then looks at how choreography can solve those problems. Allen explores three approaches to choreography: HTTP based, pub/sub messaging based, and brokerless swarming systems. He introduces appropriate messaging architectures and frameworks and looks at several practical examples. Finally, Allen looks at event storming: one of the best approaches to designing choreographed systems. You’ll leave with an understanding of both why you should be using choreography for most high-level APIs and how to design and build these systems. This session was recorded at the 2019 O'Reilly Software Architecture Conference in San Jose.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 70
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    Beyond accidental architecture (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 1 hr., 18 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Software systems always express some form of architecture. Many times those architectures reflect the mere circumstances and microtrends prevailing at various times. But long-term success doesn’t happen by accident. When approached deliberately, software architecture and design can produce benefits for teams in a variety of ways. James Thompson (Cingo Solutions) demonstrates how to assess approaches and make decisions based on what matters to your team and your projects by answering the following guiding questions: What is software architecture? What decisions are architectural? How do you discuss software architecture? How do you document architectural decisions? How do you encourage continued learning? These questions give you a framework for thinking about how to do software architecture in a collaborative way. Software development is a collaborative effort, and software architecture should be also. Software architecture is something that every developer should be equipped and empowered to engage with—leading to a more collaborative way of developing and maintaining your software systems. This session was recorded at the 2019 O'Reilly Software Architecture Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 71
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    Spotlight on Innovation : The Future Beyond Digital—Entering a New Era of Exploration and Collaboration with Greg Satell (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 56 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Innovation isn’t just about solving existing problems faster and more efficiently; it’s about discovering and solving new ones. To remain competitive, today’s business leaders need to embrace not only new technologies but an entirely new mindset when it comes to innovation. Recorded on November 28, 2018. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Innovation , you’ll discover what successful companies have in common and how you can follow their lead with small practical steps to transform your organization and prepare for the Next Economy.
    Anmerkung: Online resource; Title from title screen (viewed August 1, 2019)
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  • 72
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    Spotlight on Innovation : AI Trends with Roger Chen (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 59 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Artificial intelligence can be intimidating. There are complex, sticky challenges of data collection and verification, algorithms and bias, and deploying AI services to production, plus ever-expanding ethical concerns. New trends in hardware, algorithms, tools, and data ecosystems are quickly transforming AI into a must-have for today’s organizations. Progress is accelerating, but so are obstacles; a successful, real-world implementation of AI depends on balancing many interdependent concerns. Recorded on March 13, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Innovation , you’ll discover what successful companies have in common and how you can follow their lead with small practical steps to transform your organization and prepare for the Next Economy.
    Anmerkung: Online resource; Title from title screen (viewed August 1, 2019)
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  • 73
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    Spotlight on Innovation : How Blockchain Will Change Your Business with Alison McCauley (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 53 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Blockchain: there’s a lot of excitement around this technology that decentralizes data, and even more opportunity. Ultimately, a business and social strategy that integrates blockchain can have a major impact on your organization, making it more efficient and increasing transparency. Recorded on January 9, 2019. See the original event page for resources for further learning. Find future live events to attend or watch recordings of other past events . O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. In live conversations, participants share their questions and ideas while hearing the experts’ unique perspectives, insights, fears, and predictions for the future. In every edition of Spotlight on Innovation , you’ll discover what successful companies have in common and how you can follow their lead with small practical steps to transform your organization and prepare for the Next Economy.
    Anmerkung: Online resource; Title from title screen (viewed August 1, 2019)
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  • 74
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    Responsible microservices (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 1 hr., 27 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: These days, you can’t swing a dry-erase marker without hitting someone talking about microservices. Developers are studying Eric Evans’s prescient book Domain-Driven Design . Teams are refactoring monolithic apps, looking for bounded contexts, and defining a ubiquitous language. And while there have been countless articles, videos, and talks to help you convert to microservices, few have spent any appreciable time asking if a given application should be a microservice. Nathaniel Schutta (Pivotal) shows you a set of factors you can apply to help you decide if something deserves to be a microservice or not. You’ll also look at what you need to do to maintain a healthy micro(services)biome. This session was recorded at the 2019 O'Reilly Software Architecture Conference in San Jose.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 75
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    Real-time analytics at Uber : Bring SQL into everything (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 40 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: From determining the most convenient rider pickup points to predicting the fastest routes, Uber uses data-driven analytics to create seamless trip experiences. Uber’s analysts and engineers wanted to run real-time analytics with deep learning models. But copying data from one source to another is pretty expensive. Zhenxiao Luo explains how Uber supports real-time analytics with deep learning on the fly, without any data copying. He starts with the company’s big data infrastructure, specifically Hadoop, Spark, and Presto, and discusses how Uber uses Presto as an interactive SQL engine and deployed Hadoop Distributed File System, Pinot, MySQL, and Elasticsearch as storage solutions. He then details how Uber built a Presto Elasticsearch connector from scratch to support real-time analytics on heterogeneous data. He concludes by sharing the company’s production experience and roadmap. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 76
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    O'Reilly Velocity Conference 2019 - Berlin, Germany (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 2 hr., 58 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: We’ll make the Keynotes available here as soon as possible after they happen. Video of the sessions and tutorials will be available a few weeks after the end of the conference. O'Reilly Velocity Conferences are devoted to providing its international audience of SREs, app developers, DevOps practitioners, systems architects, CTOs, and CIOs with the most on-point training and information possible on how to build and maintain large-scale cloud native systems. Velocity Berlin 2019 stayed true to this formula. It gathered some of the world's top cloud practitioners to share their expertise and insights on key concepts like Kubernetes, site reliability engineering, observability, and performance. Stay ahead of your competition, get this video compilation, and enjoy a front-row seat to all of the best that Velocity Berlin 2019 had to offer. Highlights include: Unrestricted access to hours of the best presentations from Velocity Berlin 2019—this video compilation includes keynote sessions, deep-dive tutorials, and technical sessions. Kubernetes sessions, including Jonathan Johnson's (Dijure LLC) intro to Kubernetes tutorial; Jose Nino’s (Lyft) look at deploying hybrid topologies with Kubernetes and Envoy; and Bastian Hofmann’s (SysEleven) talk on using Kubernetes to deploy an application across multiple clusters in different regions. Monitoring, Observability, and Performance sessions, such as Liz Fong-Jones (Honeycomb) on how microservice-based systems do distributed tracing using OpenTelemetry; Lorenzo Fontana (Sysdig) on eBPF-powered distributed Kubernetes performance analysis; and Nathanael Jean-Francois (NS1) on using BGP edge optimizations to measure internet performance and make routing decisions. Overcoming Obstacles/Lessons in Resilience sessions, including Alois Reitbauer’s (Dynatrace Software) cautions on how to ensure that delivery pipeline automation code won’t devolve into legacy code and Josh Michielsen’s (Condé Nast International) insider’s take on the lessons learned from operating a Kubernetes driven global cloud native platform. Building Secure Systems sessions like Jennifer Davis’ (Microsoft) survey of the cloud security tools and practices everyone should. Building Resilient Systems sessions, such as Jenn Strater’s (Gradle) guide to using data to better debug build errors, speed up individual runs, and make a happy release process, or Heidi Waterhouse’s (LaunchDarkly) reveal of the often overlooked factors that dramatically affect...
    Anmerkung: Online resource; Title from title screen (viewed November 6, 2019)
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    O'Reilly Software Architecture Conference 2019 - Berlin, Germany (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 1 hr., 49 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: We’ll make the Keynotes available here as soon as possible after they happen. Video of the sessions and tutorials will be available a few weeks after the end of the conference. Thousands of software architects, aspiring software architects, senior developers, system leads, tech leads, software engineers, data architects, and tech team managers came together at O’Reilly Media’s Software Architecture Conference Berlin 2019 to learn how to design, build, and improve the architectures that are the foundation of all modern software systems. Who did the attendees learn from? They learned from experts like senior software architect Carola Lilienthal (Workplace Solutions), a 30-year software engineering vet, who broke down the causes and costs of technical debt and then outlined the ways to eliminate it. And they learned from site reliability expert Jibby Ayo-Ani (Welkin) as she described Google’s BeyondCorp, a new enterprise security model in cloud computing that every software architect needs to understand. These speakers and gave SACON Berlin 2019’s attendees what they hoped to receive: The knowledge and insights required to become the best software architects they could be. Get this video compilation of SACON Berlin 2019 and you’ll gain the opportunity to increase your skill set and move forward in your software architecture career. Highlights include: Complete video coverage of SACON Berlin 2019’s best keynotes, tutorials, and technical sessions—this compilation contains hours of SACON talks to study and absorb at your own pace and schedule. Keynote addresses from software architecture’s most incisive thinkers, including Bosatsu Consulting President Brian Sletten, ThoughtWorks principal technology consultant Zhamak Dehghani, and the Cloud Native Computing Foundation’s Director of Ecosystem, Cheryl Hung. Hands-on tutorials covering subjects like reusable information architecture; event storming for domain-driven design modeling; and building, specifying, and testing APIs with microservices. Application Architecture sessions, including Patrick Kua (Chief Scientist, N26) on scaling out architectural decision-making during rapid growth; Vladik Khononov (Chief Architect, Naxex) on DDD’s most common mistakes; and Paddy Fagan (Chief Architect, IBM Watson Care Manager) on implementing continuous architectural refactoring in a SaaS offering. Distributed Systems sessions, such as on the need for autonomous APIs in a landscape of impossibly complex distribu...
    Anmerkung: Online resource; Title from title screen (viewed November 6, 2019)
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  • 78
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    ML at Twitter : A deep dive into Twitter's timeline (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 41 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Machine learning has allowed Twitter to drive engagement, promote healthier conversations, and deliver catered advertisements. Cibele Montez Halasz and Satanjeev Banerjee describe one of those use cases: timeline ranking. They share some of the optimizations that the team has made—from modeling to infrastructure—in order to have models that are both expressive and efficient. You’ll explore the feature pipeline, modeling decisions, platform improvements, hyperparameter tuning, and architecture (alongside discretization and isotonic calibration) as well as some of the challenges Twitter faced by working with heavily text-based (sparse) data and some of the improvements the team made in its TensorFlow-based platform to deal with these use cases. Join in to gain a holistic view of one of Twitter’s most prominent machine learning use cases. This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 79
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    Interpretable and resilient AI for financial services (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 48 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Financial services are increasingly deploying AI models and services for a wide range of applications in the credit lifecycle, such as credit onboarding and identifying transaction fraud and identity fraud. These models must be interpretable, explainable, and resilient to adversarial attacks. In some situations, regulatory requirements apply that prohibit black-box machine learning models. Jari Koister (FICO) shares forward-looking tools and infrastructure has developed to support these needs. Topics include: Examples of financial services applications of AI and ML Specific needs for explainability and resiliency Approaches for solving explainability and resiliency Regulatory requirements and how to meet them A platform that provide support for xAI and mission-critical AI Further research and product development directions This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
    URL: lizenzpflichtig
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  • 80
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    Expert Playlist Intro : Learn How To Write A Great Technical Book, By Example (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 3 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: In this brief introduction to Tim O'Reilly's Expert Playlist , find out why he selected these books as great examples of how to write a technical book.
    Anmerkung: Online resource; Title from title screen (viewed August 25, 2019)
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  • 81
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    Executive Briefing : Why machine-learned models crash and burn in production and what to do about it (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 37 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Much progress has been made over the past decade on process and tooling for managing large-scale, multitier cloud apps and APIs, but there is far less common knowledge on best practices for managing machine-learned models (classifiers, forecasters, etc.), especially beyond the modeling, optimization, and deployment process once these models are in production. A key mindset shift required to address these issues is understanding that model development is different than software development in fundamental ways. David Talby (Pacific AI) shares real-world case studies showing why this is true and explains what you can do about it, covering key best practices that executives, solution architects, and delivery teams must take into account when committing to successfully deliver and operate data science-intensive systems in the real world. Topics include: Concept drift (Machine-learned models begin degrading as soon as they’re deployed and must adapt to a changing environment.) Locality and limited reuse and generalization of models A/B testing challenges, which make it very hard in practice to know which model will perform better in production Semisupervised and adversarial learning scenarios, which require modeling and optimizing models only once they’re in production The impact of all of the above on product planning, staffing, and client expectation management This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
    URL: lizenzpflichtig
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  • 82
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    Enabling insights and analytics with data streaming architectures and pipelines using Kafka and Hadoop (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 40 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: In a large global health services company, streaming data for processing and sharing comes with its own challenges. Data science and analytics platforms need data fast, from relevant sources, to act on this data quickly and share the insights with consumers with the same speed and urgency. Join Mohammad Quraishi (Cigna) to learn why streaming data architectures are a necessity—Kafka and Hadoop are key. Mohammad outlines architectures centered around the Hadoop Platform and Kafka that were implemented to support a variety of integration and analytics requirements. Topics include: Enabling streaming to and from relational sources and files using custom frameworks that automate and speed up workflows Combining the polyglot techniques with Kafka API to support various streaming solutions Combining data driven techniques to support consumers through a simple streaming architecture and microservices How HBase, Kudu, and Kafka Streams are used to reduce latency between these microservices and frontend application APIs Enabling the consumption and sharing of data sources and results using streams Enabling Spark Structured Streaming, Flink, and Spark ML on these streams Enabling data sync between on-premises data lakes and the cloud Supporting cloud native architectures that enable machine learning in the cloud This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
    URL: lizenzpflichtig
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  • 83
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    Building enterprise data products (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 44 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Much of the hardest work of creating effective data products in the enterprise is not in the complexity of the algorithms applied but in effective design and integration into downstream systems. Hilary Mason (Cloudera) shares a process for repeatedly creating effective AI products, from idea through process to specific design considerations, and explains how architecture and algorithmic choices can support or hinder this process. This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
    URL: lizenzpflichtig
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  • 84
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    Bringing data to life : Combining machine learning and art to tell a data story (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 24 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Analytics and AI are powerful methods for extracting insights hidden in data. However, these methods by themselves cannot convey insights. Visualization is a key requirement for explaining analytical findings, but visualizations such as graphs and charts are not always enough to explain data, especially to a nontechnical audience. This audience may need a different approach to connect with the data. Nancy Rausch (SAS Institute) shares a case study for a project that combined machine learning and art to tell a big data story. She explains how she and her team collected and prepared IoT streaming data from a solar array farm, applied an analytical model to forecast future output, and then visualized the results for general audiences using interactive art. They also used artificial intelligence and natural language processing to allow visitors to interact with the art installation. The project brought solar array technology to life in a way that was able to engage and delight visitors of all ages and backgrounds. This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
    URL: lizenzpflichtig
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  • 85
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    Applied machine learning in finance - 2019 Artificial Intelligence Conference, New York (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 45 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed by both sell-side and buy-side institutions. Techniques like time series analysis, stochastic calculus, multivariate statistics, and numerical optimization are often used by "quants” for modeling asset prices, portfolio construction and optimization, and building automated trading strategies. Chakri Cherukuri (Bloomberg LP) demonstrates how to apply machine learning techniques in quantitative finance, covering use cases involving both structured and alternative datasets. The focus of the talk will be on promoting reproducible research (through Jupyter notebooks and interactive plots) and interpretable models. This session was recorded at the 2019 O'Reilly Artificial Intelligence Conference in New York.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
    URL: lizenzpflichtig
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  • 86
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    An architect's guiding principles for leadership (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 44 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Architects are leaders. We need to understand our business, our users, and our ecosystem. We need to effectively interact with our stakeholders, productively collaborate with design and product teams, and give direction and motivate our development teams. Achieving these goals requires much more than technical excellence. Seth Dobbs (Bounteous) shares a primer on leadership for architects, focusing on guiding principles that are easy to learn and put into practice. Principles cover both inward focus (personal mastery) and external focus (effective interactions) and include topics such as vision, problem solving, ownership, and conflict. Seth explores material he’s used to train team members ranging from developers and designers up into the executive level but focuses on core guiding principles specific to helping architects be more effective in their roles. Each principle will be presented first with a problem or anti-pattern, then the principle, and then examples of the principle in practice. This approach should be valuable whether you are new in a leadership role and looking to understand the “soft” skills required or have been leading for a while and want to formalize your thinking on leadership. This session was recorded at the 2019 O'Reilly Software Architecture Conference in San Jose.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 87
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    Brains considered harmful : How cognitive biases undermine your software architecture (2019)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 45 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Most software engineers come up through the ranks as coders and believe that the valuable lessons they’ve learned from their years in the trenches are an infallible guide to the future. While experience is certainly useful, the emerging field of cognitive psychology has another story to tell: the real reasons for our decisions aren’t entirely the subject of our conscious choice—or even awareness. Ian Varley (Salesforce) covers the emerging field of cognitive biases—bugs in our mental operating system—and takes a cold, hard look at how these mental blind spots defeat our attempts to build quality software in every domain. (If you’ve read books like Thinking Fast and Slow and You Are Not So Smart , you’ll be familiar with the basic idea.) While awareness of cognitive biases is a good life skill in general, it’s particularly critical if you’re in a software architect role, because your opinions set the conditions for massive amounts of work by other engineers. As such, it’s worth the time to thoroughly debug your own process for learning and making important decisions. Ian explains why the sunk cost fallacy means you’re not throwing things out fast enough; how confirmation bias can sneak into even the most data-driven decisions; how hindsight bias is obscuring the real lessons you might have learned from that failed project; how priming and fixation is shooting down your most promising inputs; and how arguing over architectural decisions is unlikely to help anybody. (See also: “Nobody ever changed their mind between Vim and Emacs.”) Most importantly, Ian shares concrete techniques you can use to check your own decision making for these unwelcome guests. We might not be capable of being perfectly rational beings, but we can be a lot less dumb. This session was recorded at the 2019 O'Reilly Software Architecture Conference in San Jose.
    Anmerkung: Online resource; Title from title screen (viewed October 31, 2019)
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  • 88
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    What motivates people (2018)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 4 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: You discover motivation science’s great breakthrough: psychological needs. These three needs are at the heart of human thriving and form the horizontal axis of the Spectrum of Motivation model.
    Anmerkung: Online resource; Title from title screen (viewed June 27, 2018)
    URL: lizenzpflichtig
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  • 89
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    Meeting hacks (2018)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 23 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Meetings are a necessary evil in an architect's life. This segment delves into some techniques you can use in meetings imposed on you and meetings you impose on others.
    Anmerkung: Online resource; Title from title screen (viewed June 25, 2018)
    URL: lizenzpflichtig
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  • 90
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    Your team's success is your success (2018)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 2 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: In this video segment, we discuss one of the most important things for a product manager to do: see the success of their team and their organization as proof of their own success.
    Anmerkung: Online resource; Title from title screen (viewed June 25, 2018)
    URL: lizenzpflichtig
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  • 91
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    What makes a great product manager? (2018)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 3 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: What are the characteristics of a great product manager? In this video segment, we look at some of the high-level attitudes and approaches that great product managers consistently bring to their work.
    Anmerkung: Online resource; Title from title screen (viewed June 25, 2018)
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  • 92
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    What does a product manager actually do all day? (2018)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 4 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Product roles can have many different names from organization to organization. In this video, we briefly cover different titles that product people might have, accounting for the remarkable range and inconsistency these titles can have.
    Anmerkung: Online resource; Title from title screen (viewed June 25, 2018)
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  • 93
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    The importance of technical onboarding, training, and mentoring (2018)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 33 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Technical onboarding is critical for the productivity and growth of engineering teams, yet it is often overlooked. Onboarding improves employee retention and satisfaction. A lack of onboarding hurts those who are different from the existing team and negatively affects diversity. Kate Heddleston explores how to best approach technical onboarding, training, and mentoring
    Anmerkung: Online resource; Title from title screen (viewed June 25, 2018)
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  • 94
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    Velocity Conference - London, UK 2018 (2018)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 39 hr., 27 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Velocity conferences, the O'Reilly symposiums devoted to web operations, DevOps, and systems engineering, always promise education with inspiration. Velocity London 2018 kept that promise. The inspiration portion came from keynote luminaries like World Economic Forum AI expert Omoju Miller, University of Cambridge distributed systems researcher Martin Kleppmann, and chaos engineering wizard Crystal Hirschorn (Condé Nast International). The education portion came in the form of the conference's sessions devoted to building, deploying, and running complex, distributed applications and systems. This video compilation offers a front row view for all of Velocity London's best keynotes, tutorials, and technical sessions. It contains hours of material to study and enjoy at your own pace. Highlights include: Kubernetes sessions, such as Bridget Kromhout's (Microsoft) intro level Kubernetes 101 tutorial; Bridget Lane's (Gannett USA Today) look at the hidden costs of Kubernetes; and Lee Calcote's (SolarWinds) tutorial on building secure microservices with Istio and Kubernetes. Distributed systems sessions, including Amy Boyle (New Relic) on building distributed real-time stream processing systems and José Carlos Chávez's (Typeform) explanation of distributed tracing. Distributed data sessions, including Rafael Chacon (Slack) on sharding MySQL clusters using Vitess and Heidi Waterhouse (LaunchDarkly) on the risks of retaining deprecated codebases. Monitoring, observability, and performance sessions, including Marcus Barczak (Fastly) on moving from push-based monitoring to pull-based with Prometheus and Constance Caramanolis (Lyft) on using Envoy’s extensive metrics to identify the root cause of production incidents. DevOps and SRE sessions, such as Ana Medina's (Gremlin) chaos engineering tutorial and Yaniv Aknin's (Google Cloud) how-to on applying metrics and data to architectural decision making. Building secure systems sessions, including Sabree Blackmon's (Scytale) look at building trust between distributed systems with SPIFFE and Mandi Walls' (Chef) deep dive tutorial into security and compliance using InSpec. Systems engineering and architecture sessions, including Siddharth Ram's (Intuit) review of how Intuit moved millions of customers from private infrastructure to the public cloud. Serverless sessions, including Yan Cui's (DAZN) detailed tutorial on how to build observability into a serverless application. Microservices and containers sessions,...
    Anmerkung: Online resource; Title from title screen (viewed November 1, 2018)
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  • 95
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    The sprint planning meeting (2018)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 2 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Before starting a sprint, it's a best practice to present the sprint to business stakeholders at a sprint planning or roadmap meeting. This final check-in will give stakeholders the opportunity to weigh in on the selected features, suggest other items that may require more immediate attention, and discuss the overall direction of the product and team. In this segment, you'll learn how to facilitate a successful sprint planning meeting.
    Anmerkung: Online resource; Title from title screen (viewed June 26, 2018)
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  • 96
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    Learn from the Experts about Product Management : Matthew Edgar (2018)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 20 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: In this Learn from the Experts interview, Ally MacDonald talks with Matthew Edgar about various aspects of product management, including: common obstacles in the field, the impact on customer experience, and resources to get you started on the path to being a great product manager.
    Anmerkung: Online resource; Title from title screen (viewed April 18, 2018)
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  • 97
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    Debugging teams (2018)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 47 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Software engineering is a team sport, and a team’s culture deeply affects each contributor’s productivity and happiness. We’ll discuss specific best practices for building strong, self-sustaining cultures. We’ll also talk about how to lead your reports rather than “managing” them, and exactly what sort of things great leaders do and don’t do in building high-functioning teams. You’ll learn why investing in these soft skills are at least as important as technological factors when it comes to success.
    Anmerkung: Online resource; Title from title screen (viewed June 25, 2018)
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  • 98
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    Mining the Social Web : Mining Instagram (2018)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
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    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 45 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: Instagram is one of the world's largest and most popular social networks with tens of millions of photos uploaded to its photo sharing platform every day. In this course, you'll learn the basics of connecting to the Instagram platform, explore its data, and analyze its content. First, you'll create a developer account and connect to the Instagram API to pull data. Then, you'll discover some techniques for analyzing that data before exploring some computer vision applications in a very accessible way. Learners must have their own Instagram profile with multiple image posts and basic proficiency in Python. Learn how to use Instagram's developer platform, API, and sandbox Explore basic techniques used in computer vision and artificial intelligence Gain experience using Google's deep learning APIs to automatically label images and detect faces Enhance your ability to build applications that can mine data from social media platforms Mikhail Klassen refined his expertise in data mining, data analysis, and machine learning during his work as a PhD in astrophysics. He's now the Chief Data Scientist for Paladin:Paradigm Knowledge Solutions in Montreal, where he combines data mining and artificial intelligence to deliver personalized training to aerospace engineers.
    Anmerkung: Online resource; Title from title screen (viewed January 11, 2018)
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  • 99
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    Learn from the Experts about Product Management : Matt LeMay (2018)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 21 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: In this Learn from the Experts interview, Ally MacDonald talks with Matt LeMay about various aspects of product management, including: common obstacles in the field, the impact on customer experience, and resources to get you started on the path to being a great product manager.
    Anmerkung: Online resource; Title from title screen (viewed April 18, 2018)
    URL: lizenzpflichtig
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
    BibTip Andere fanden auch interessant ...
    Online-Ressource
    MPI Ethno. Forsch.
  • 100
    Online-Ressource
    Online-Ressource
    Learn from the Experts about Product Management : Ben Gaines (2018)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Sprache: Englisch
    Seiten: 1 online resource (1 video file, approximately 26 min.)
    Ausgabe: 1st edition
    Schlagwort(e): Electronic videos ; local
    Kurzfassung: In this Learn from the Experts interview, Ally MacDonald talks with Ben Gaines about various aspects of product management, including: common obstacles in the field, the impact on customer experience, and resources to get you started on the path to being a great product manager.
    Anmerkung: Online resource; Title from title screen (viewed April 18, 2018)
    URL: lizenzpflichtig
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
    BibTip Andere fanden auch interessant ...
    Online-Ressource
    MPI Ethno. Forsch.
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