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  • 2020-2024  (14)
  • 2020-2022
  • 1980-1984
  • O'Reilly 〈Firm〉,  (14)
  • Machine learning  (14)
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Year
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  • 1
    Online Resource
    Online Resource
    Discover Data Science with Python Essentials : Data Acquisition and Visualization with Machine Learning (2024)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 57 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Machine learning ; Computer programming ; Python (Langage de programmation) ; Apprentissage automatique ; Programmation (Informatique) ; computer programming ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: In this course, you will learn to use Python for data science and gain the essential skills to analyze and visualize data effectively. Whether you are a data analyst, data scientist, business analyst, or data engineer, this course will provide you with the knowledge and tools to excel in your role. Python is a versatile language that offers powerful libraries for data manipulation, visualization, and machine learning, making it the go-to choice for data professionals. The course solves the problem of understanding and leveraging Python's capabilities for data science by providing business use cases. You will learn to use popular Python libraries such as Pandas, Matplotlib, Seaborn, and Scikit-learn to analyze and visualize data, perform statistical analysis, and build predictive models. By the end of the course, you will have a solid foundation in Python for data science and be ready to apply your skills to real-world projects. What you'll learn and how you can apply it Upon completion of this course, learners will be able to: Apply Python programming concepts for data analysis and visualization Manipulate and analyze data using Pandas Create informative and visually appealing data visualizations using Matplotlib and Seaborn Perform statistical analysis to gain insights from the data Build and evaluate machine learning models for predictive analytics This course is for you because... You're a Python beginner who wants to learn how to manage data with Python. You're a traditional data analyst who has experience with tools like Excel and Tableau, but wants to learn how to manage data with Python. You're a finance, healthcare, ecommerce, or manufacturing/logistics professional looking to become adept in Python and data science. Prerequisites No prior knowledge of Python or Data analytics is needed. All course files can be accessed in this GitHub repository.
    Note: Online resource; title from title details screen (O'Reilly, viewed April 23, 2024)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    What Is logistic regression in 3 minutes? (2024)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (4 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: Deep learning (Machine learning) ; Machine learning ; Logistic regression analysis ; Apprentissage profond ; Apprentissage automatique ; Régression logistique ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: These videos are a series of shorts explaining critical data science concepts through visuals and concise explanations. By learning these fundamentals in bite-sized format, you can digest concepts that are core in machine learning and statistical modeling. Problems like bias and spurious correlations can become easier to grasp as well. Topics include Bayes Theorem, probability distributions, mathematical notation, correlation coefficients, linear regression, and logistic regression.
    Note: Online resource; title from title details screen (O'Reilly, viewed April 16, 2024)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    O'Reilly Book Club : Aurélien Géron on hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (2023)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (59 min.)) , sound, color.
    Edition: Third edition.
    DDC: 006.3/1
    Keywords: Machine learning ; Machine learning ; Instructional films ; Internet videos ; Nonfiction films ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Join us for this edition of O'Reilly Book Club with Aurélien Géron, author of Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, to learn about the fundamentals of machine learning. You'll cover everything from working with real data to training and deploying models as you learn tricks of the trade, listen to stories, ask your questions, and connect with other readers. What you'll learn and how you can apply it Learn the concepts, tools, and techniques for doing end-to-end machine learning Explore the latest deep learning technologies, such as vision transformers, multimodal models, and more Find out how to update your machine learning systems for state-of-the-art performance This recording of a live event is for you because... You want to go beyond the words on the page and hear from the expert. You're a machine learning practitioner who wants to learn about the latest developments in the field, including updates to scikit-learn and Keras, large language models, and new generative learning tools such as diffusion models. Recommended follow-up: Read Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, third edition (book) Watch Natural Language Processing Using Transformer Architectures (video) Read TensorFlow 2 Pocket Reference (book).
    Note: Online resource; title from title details screen (O'Reilly, viewed June 26, 2023)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    AWS Redshift data platform fundamentals (2023)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 29 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3
    Keywords: Amazon Web Services (Firm) ; Machine learning ; Cloud computing ; Instructional films ; Nonfiction films ; Internet videos ; Amazon Web Services (Firm) ; Machine learning ; Cloud computing ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Enterprise organizations are continually shifting to innovative cloud services that will provide them with a rapid, efficient, scalable and industry leading solution. Amazon Redshift is a petabyte scale data platform as a Service (PaaS) that delivers high performance enterprise focused data services that are hosted and are fully scalable solutions without placing a massive investment in your enterprise infrastructure. Amazon Redshift also provides robust access to a variety of data analytics tools, compliance features, integration with other AWS services and even artificial intelligence/ machine learning applications that allow customers numerous opportunities to derive value with their current business intelligence tools. In this course you will learn everything you need to know to get started using Amazon Redshift to deploy a data warehouse/data lake and provide immediate value for your enterprise. What you'll learn and how you can apply it Understand why data warehouses are so critical to enterprise organizations and how Amazon Redshift provides immediate value for enterprises that use it. Identify the critical differences between a traditional enterprise data warehouse and Amazon Redshift. Learn the Column-Oriented file system structures and the database options available with Amazon Redshift. Learn how to determine proper enterprises use cases for using AWS Redshift focused on uses such as ETL, Data Transformation, Data Mining, Data Lakes and Business Intelligence. Describe AWS enterprise best practices for cloud security, networking, and IAM with Amazon Redshift. Learn the critical components and the essential features of AWS Redshift (Query Editor, Optimizer, Spectrum) Understand schemas, structures, SQL options, monitoring options, etc. with Amazon Redshift. Determine the proper node and cluster deployment option(s) for your Amazon Redshift requirements. Understand the options for connecting to the data warehouse and for querying the data warehouse. This course is for you because... You need to understand how to simplify a data warehouse infrastructure with AWS. You're an aspiring data engineer, database architect, cloud developer, cloud architect, or other technical professional that wants to get started with Amazon Redshift. You work with AWS services daily and would like to integrate with other AWS or Partner services with AWS Redshift successfully. You want to obtain an AWS Specialty Certification and would like to obtain knowledge and practice around Amazon Redshift for the AWS Certified Database or Data Analytics Specialty exams. This course will help with those certification since it covers over 15% of the exam objectives. Prerequisites: 6 Months of AWS "Hands on Experience" with AWS Services would be advisable. Basic SQL Database "Hands on Experience" around relational database is needed. An AWS Paid Account to be able to perform the exercises and following along with the demonstrations. Some of the services with Amazon Redshift would not be fully covered with an AWS Free Tier account.
    Note: Online resource; title from title details screen (O'Reilly, viewed Decenber 5, 2023)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 5
    Online Resource
    Online Resource
    Extending ChatGPT with the Rest API and plug-ins (2023)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 35 min.)) , sound, color.
    Edition: [First edition].
    Series Statement: Live courses
    DDC: 006.3/5
    Keywords: ChatGPT ; Natural language generation (Computer science) Computer programs ; Neural networks (Computer science) ; Plug-ins (Computer programs) ; Web applications Development ; Machine learning ; Machine learning ; Neural networks (Computer science) ; Plug-ins (Computer programs) ; Instructional films ; Internet videos ; Nonfiction films ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: You can accomplish a lot with ChatGPT just by typing commands into a website, but ChatGPT also exposes a REST API that makes it possible to embed its intelligence into your apps. Imagine writing tools that translate product manuals to other languages or convert VB.NET code to C#. Or what about putting a ChatGPT frontend on your company's SharePoint documents? The ChatGPT API makes all this possible. ChatGPT plug-ins are bits of code that tell ChatGPT how to use an external resource on the internet. They can direct ChatGPT to access up-to-date information, run computations, or use third-party services and to interact with APIs defined by developers, enhancing ChatGPT's capabilities and allowing it to perform a wide range of actions. Done right, they can help businesses provide exceptional customer experiences, increase sales, automate redundant tasks, enhance brand loyalty, and much more. Plug-ins represent an opportunity to build an "app store" for ChatGPT. Along with the ChatGPT REST API, they're the sweet sauce that infuses modern apps and sites with the wizardry that ChatGPT and large language models seem to be bringing to the forefront of computing today. Join Fixie.ai's Matt Welsh, Nicole Butterfield from O'Reilly and other leading experts in the field to learn more about these plug-ins and get in on the ground floor of this explosive new technology. What you'll learn and how you can apply it Explore current ChatGPT plug-ins and use cases Learn how to incorporate plug-ins into your developer workflow Get hands-on insights with rich interactive demos Understand the basics of REST API development This live course is for you because... You're a machine learning product owner or practitioner or a developer building new applications or products with state-of-the-art AI. You want to learn more about ChatGPT plug-ins and understand how they can enhance the tool's capabilities. Recommended follow-up: Read Natural Language Processing with Transformers, revised edition (book) Read Generative Deep Learning, second edition (book) Read Hands-On Generative AI with Transformers and Diffusion Models (book) Read "Serving" (chapter 8 in Reliable Machine Learning) (book) Follow ChatGPT (expert playlist) Read What Are ChatGPT and Its Friends? (report) Read RESTful Web API Patterns and Practices Cookbook (book) Read Mastering API Architecture (book) Read Developing Apps with GPT-4 and ChatGPT (early release book) Please note that slides or supplemental materials are not available for download from this recording. Resources are only provided at the time of the live event.
    Note: Online resource; title from title details screen (O'Reilly, viewed June 26, 2023)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 6
    Online Resource
    Online Resource
    Python for finance (2023)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (4 hr., 7 min.)) , sound, color.
    Edition: [First edition].
    DDC: 658.155
    Keywords: Financial risk management ; Machine learning ; Python (Computer program language) ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Python programming knowledge is at the core of financial applications. Knowing Python and modeling will help you to tackle the challenging problems in finance. Besides, learning or improving the modeling skill in finance with Python can enhance your understanding and contribute tremendously to your skill set. Python is a powerful tool in modeling due to its simplicity and robust modeling capabilities. Python includes libraries for mathematical operations, optimization, visualization, manipulation, and so on. Combining these wide range of applications with a user-friendly Python environment, there has been a consensus to say that Python is quite handy in financial modeling. In this video course, modern financial issues will be tackled with step-by-step explanations via Python. To do that, the course is divided into three modules. In the first module, after a brief introduction to Python with functions, iterations, and conditions, the main financial concepts will be discussed. What you'll learn and how you can apply it How to write script for the main functions of Python. What the main financial tools in financial modeling are (as well as Regression, APIs, and required Python libraries). How to tackle main Financial problems with Python. How to interpret the empirical results of the financial model and how to make sense of them. This course is for you because... You're a financial analyst or decision maker in your current role. You want to improve your finance knowledge. You're familiar with Python and specifically want to learn how to adapt Python to Finance while increasing your Python skills. Knowing financial modeling takes you to a whole new level in your career. Prerequisites: Beginner level Python Beginner level of finance knowledge.
    Note: Online resource; title from title details screen (O'Reilly, viewed August 14, 2023)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 7
    Online Resource
    Online Resource
    O'Reilly Book Club : Chip Huyen on designing machine learning systems-reliable, scalable, and adaptive machine learning systems (2023)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (55 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: Machine learning Development ; Machine learning ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Join us for this edition of O'Reilly Book Club with Chip Huyen, author of Designing Machine Learning Systems. You'll learn more about the complex and quickly evolving components of current machine learning systems and how best to ensure your systems are scalable, reliable, and taking advantage of the developing technologies in the field. Ask questions, learn tricks of the trade, listen to stories, and connect with other readers. What you'll learn and how you can apply it Discuss the challenges you're facing and discover opportunities for building machine learning systems for industry Learn tips for developing reliable, scalable, and adaptive machine learning systems for changing environments and business requirements This live course is for you because... You want to go beyond the words on the page and hear directly from the subject matter expert. You're a data scientist, data/machine learning engineer, machine learning architect, or software engineer who is building systems on top of AI models. Recommended follow-up: Read Designing Machine Learning Systems (book) Read Machine Learning Interviews (early release book) Read Machine Learning Design Patterns (book) Watch Designing Machine Learning Systems (Superstream video) Follow AI Superstream: Designing Machine Learning Systems (expert playlist) Please note that slides or supplemental materials are not available for download from this recording. Resources are only provided at the time of the live event.
    Note: Online resource; title from title details screen (O'Reilly, viewed September 05, 2023)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 8
    Online Resource
    Online Resource
    O'Reilly Book Club : Lewis Tunstall and Leandro von Werra on natural language processing with transformers (2023)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (1 hr., 24 min.)) , sound, color.
    Edition: Revised edition.
    DDC: 006.3/5
    Keywords: Natural language processing (Computer science) ; Machine learning ; Cloud computing ; Traitement automatique des langues naturelles ; Apprentissage automatique ; Infonuagique ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: Sponsored by deepset Join us for this edition of O'Reilly Book Club with Lewis Tunstall and Leandro von Werra, authors of Natural Language Processing with Transformers, to learn how transformers, which are the backbone of the state-of-the-art LLM models in the field today, work and how to integrate them in your applications. Bringing insights from their experience working at Hugging Face, Lewis and Leandro will discuss the benefits and challenges of training your own model, scaling, and deploying it for your system. Learn tricks of the trade, listen to stories, and gain new insights. What you'll learn and how you can apply it Learn about a variety of NLP tasks transformers can help you solve Understand how transformers can be used for transfer learning Learn about building, debugging, and optimizing transformer models This live event is for you because... You want to go beyond the words on the page and ask your own questions. You're a data scientist, machine learning engineer, or developer who is working with AI and machine learning and want to learn about the latest developments in the field. Recommended follow-up: Read Natural Language Processing with Transformers, Revised Edition (book) Read Hands-On Large Language Models (early release book) Read Hands-On Generative AI with Transformers and Diffusion Models (early release book) Read Introduction to Transformers for NLP: With the Hugging Face Library and Models to Solve Problems (book) Watch Natural Language Processing Using Transformer Architectures (video).
    Note: Online resource; title from title details screen (O'Reilly, viewed November 1, 2023)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 9
    Online Resource
    Online Resource
    AI Superstream : Data-centric AI (2023)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 49 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: Machine learning ; Artificial intelligence ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Over the past decade, the field of AI has achieved incredible results by focusing on building and training powerful deep learning models, from convolutional neural networks to state-of-the-art transformers. While the results of this model-centric approach have been inspiring, a growing number of experts have recognized the importance of ensuring the quality of the data used to train these models in order to build real-world machine learning systems that address the business and social needs of today. AI pioneer Andrew Ng has spearheaded the effort to move away from a model-centric approach to what he calls a "data-centric" approach to solving today's AI challenges. Data-centric AI renews focus on improving the data that makes AI systems work, through data iterability and quality, by embracing programmatic approaches to data labeling and curation, and by recentering subject matter experts as key players within the AI system development process. If you're a data scientist, machine learning engineer, or another decision maker overseeing the development and deployment of machine learning systems and you've already experienced the limits of a model-centric approach, this event is for you. Join us for expert-led sessions to discover the untapped potential of data-centric AI. What you'll learn and how you can apply it Understand the principles of data-centric AI and how they can improve your machine learning systems Learn how to enhance your machine learning system through data iterability and quality, data labeling and curation, and by recentering subject matter experts This recording of a live event is for you because... You're working with data for machine learning systems as a data scientist, data/machine learning engineer, data/machine learning architect, or machine learning team leader. You want to leverage your data effectively and efficiently to get the most out of your machine learning system. Prerequisites Basic knowledge of machine learning systems Recommended follow-up: Read Training Data for Machine Learning (early release book) Read Practical Weak Supervision (book) Watch Best Practices for Automated Data Labeling in NLP (event video) Please note that slides or supplemental materials are not available for download from this recording. Resources are only provided at the time of the live event.
    Note: Online resource; title from title details screen (O'Reilly, viewed September 19, 2023)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 10
    Online Resource
    Online Resource
    TensorFlow deep dive : build, train, and deploy machine learning models with TensorFlow (2023)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 53 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: TensorFlow ; Machine learning ; Neural networks (Computer science) ; Artificial intelligence ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: TensorFlow ( tf ) is a mainstream software platform for data science, data engineering, and MLOps. Maintained by Google and the community, this open source platform has facilitated many of the dramatic advances in AI over the past ten years. TensorFlow is used extensively across academia and industry, and is one of the most prominent tools in a data scientist's utility belt today. In this course you'll learn about TensorFlow itself and how to use it in your daily work. What you'll learn and how you can apply it Understand the TensorFlow platform and the roles of its components Use TensorFlow to accomplish typical data science tasks: model training, optimization, inference, serving, monitoring and more Gain a working knowledge of TensorFlow APIs This course is for you because... You're a software engineer familiar with Python and would like to learn TensorFlow. You're a data scientist who knows Python and wants to diversify your tools. You want to become more proficient at machine learning with TensorFlow. TensorFlow is an adjacent technology that you would like to understand better. You want to use TensorFlow to build machine learning models. You want to get hands-on with TensorFlow and build and train a machine learning model. Prerequisites: Working knowledge of Python Data science experience a plus but not necessary Familiarity with NumPy a plus but not necessary Basics of machine learning a plus but not necessary An understanding of statistics and mathematics a plus but not necessary Familiarity with deep learning a plus but not necessary Recommended follow-up Learning TensorFlow (book) TensorFlow for Deep Learning (book) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition (book) Designing Data-Intensive Applications (book) Fundamentals of Data Engineering (book).
    Note: Online resource; title from title details screen (O'Reilly, viewed Decenber 19, 2023)
    URL: lizenzpflichtig
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    MPI Ethno. Forsch.
  • 11
    Online Resource
    Online Resource
    Data Science For SEO (2023)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (4 hr., 21 min.)) , sound, color.
    Edition: [First edition].
    DDC: 005.13/3
    Keywords: Mathematical optimization ; Search engines ; Python (Computer program language) ; Machine learning ; Optimisation mathématique ; Moteurs de recherche ; Python (Langage de programmation) ; Apprentissage automatique ; search engines ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: With an increased demand for SEO effectiveness, Python, and the advances in data science, this course teaches web professionals, data analysts, and scientists how to understand and apply data-driven approaches to improve website search visibility. It's crucial for those in digital marketing and data analysis to stay ahead in a rapidly evolving field where data science is key to success. This course uniquely integrates the power of Python and data science with the strategic needs of Search Engine Optimization. This course offers in-depth knowledge and practical skills for leveraging data science in enhancing SEO strategies across high demand SEO topic areas. Learners will understand how to navigate the challenges of integrating data science techniques into SEO strategies. The course addresses the gap in knowledge between SEO practices and data science, providing professionals with the tools to make informed, data-backed decisions, crucial for enhancing online website visibility in search engines. Learners will acquire skills in querying the Google Search Console API, keyword clustering based on search intent, data-driven content optimization, forecasting search trends, and conducting valid SEO split tests. The course empowers participants to use Python for practical, impactful SEO and data analysis, setting a new standard for excellence in SEO. What you'll learn and how you can apply it By the end of this course, the learner should understand the relationship between data and SEO performance and be able to apply data science techniques in Python. This course is for you because... You're a SEO professional interested in learning to save time producing high impact SEO recommendations. You're a data scientist or data analyst looking to level up your SEO skills to help your organization win and retain more clients and increase visibility. You want to see how Python and Data Science can be applied to SEO. Prerequisites Open a Jupyter notebook Run Python commands in Jupyter Install Python packages either via the command line or Jupyter using the Python Install Packages (pip).
    Note: Online resource; title from title details screen (O'Reilly, viewed January 23, 2024)
    URL: lizenzpflichtig
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    MPI Ethno. Forsch.
  • 12
    Online Resource
    Online Resource
    AI Superstream : MLOps (2022)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 51 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/1
    Keywords: Machine learning ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: MLOps is consistently one of the greatest challenges engineers face when creating and maintaining machine learning systems. Join expert practitioners to learn techniques and best practices for operationalizing machine learning models and explore case studies of them in action, showing you what works--and what doesn't. What you'll learn and how you can apply it Understand MLOps processes for model deployment, containerization, and automation as well as monitoring, continuous experimentation, and improvement Learn how an understanding of SRE and DevOps principles can enhance the practice of MLOps Avoid common pitfalls in the process of building end-to-end machine learning pipelines This recording of a live event is for you because... You're a data or machine learning practitioner who puts machine learning models into production, or you're embarking on an MLOps career path. You want to improve your process of productionizing machine learning models by applying new techniques and best practices. Recommended follow-up: Read Practical MLOps (book) Read Reliable Machine Learning (book) Watch Radar Talks: Hugo Bowne-Anderson on MLOps Versus DevOps (video) Take Practical MLOps (live online course with Noah Gift) Take Open Source MLOps in 4 Weeks (live online course with Alex Kim) Read Site Reliability Engineering (book).
    Note: Online resource; title from title details screen (O'Reilly, viewed December 12, 2022)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    MPI Ethno. Forsch.
  • 13
    Online Resource
    Online Resource
    Efficient machine learning (2022)
    [Sebastopol, California] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (3 hr., 13 min.)) , sound, color.
    Edition: [First edition].
    Series Statement: Artificial intelligence superstream
    DDC: 006.3/1
    Keywords: Machine learning ; Apprentissage automatique ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet ; Webcast
    Abstract: Sponsored by Intel Machine learning has grown significantly, and with it the footprint of ML models--which can make training, deploying, and monitoring difficult and expensive. What if you could make your ML models and systems more efficient, whether in the form of cost, compute, storage, latency, or carbon footprint? Join us for this Superstream where experts dive into techniques for using fewer resources and delivering better quality. About the AI Superstream Series: This three-part series of half-day online events is packed with insights from some of the brightest minds in AI. You'll get a deeper understanding of the latest tools and technologies that can help keep your organization competitive and learn to leverage AI to drive real business results. What you'll learn and how you can apply it Understand hardware and software resources required for deep learning Learn how to optimize ML models and workloads Discover how to build robust and scalable machine learning systems Explore AI efficiencies that combat climate change This recording of a live event is for you because... You're an ML engineer or data practitioner who wants to use more-efficient algorithms and improve ML model efficiency. You're a data team leader or CDO who wants to proactively reduce the cost and resource use of ML systems and pipelines. You're a product stakeholder who wants to learn more about how ML efficiencies align with business goals. Recommended follow-up: Read Efficient Deep Learning (early release book) Watch Data Structures, Algorithms, and Machine Learning Optimization (video).
    Note: Online resource; title from title details screen (O’Reilly, viewed March 10, 2022)
    URL: lizenzpflichtig
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    MPI Ethno. Forsch.
  • 14
    Online Resource
    Online Resource
    O'Reilly Radar event : data & AI, September 2022 (2022)
    [Place of publication not identified] : O'Reilly Media, Inc.
    Details
    Language: English
    Pages: 1 online resource (1 video file (2 hr., 29 min.)) , sound, color.
    Edition: [First edition].
    DDC: 004
    Keywords: Computer science ; Artificial intelligence ; Machine learning ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Sponsored by Robocorp In today's economy, it's vital for businesses to adapt to a digital workforce, improve resilience and operational excellence, increase scalability, and uncover new opportunities and insights. That's why Gartner considers hyperautomation one of the top strategic technology trends for 2022--and why we've gathered some of the brightest minds in the industry to explore the opportunities of hyperautomation. Join us to discover how the next generation of applied AI, hyperautomation, will enable better decisions faster and closer to the ground. You'll see how AI improves today's business intelligence tools, how real-time data streaming and augmented analytics can change decision making, and how your organization can benefit from decision intelligence.
    Note: Online resource; title from title details screen (O'Reilly, viewed September 27, 2022)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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