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  • 1
    Online Resource
    Online Resource
    AI Superstream Series : AI & ML in Production (2021)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 3 hr., 25 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: One of the most consistent challenges for ML engineers is how to move from model to production. Join us for a day of sessions dedicated to making the most of AI in your company. You’ll learn about everything from scaling to deployment and from pipeline to model decay—straight from our experts. About the AI Superstream Series: This four-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 how MLOps can help you evolve from manually building models Learn how to use PyTorch to effectively deploy and scale your AI models Explore design patterns that will help you tackle problems that frequently crop up during the ML process This Superstream is for you because... You want to learn more about moving machine learning from model to production. You want to better understand MLOps. You’re interested in improving your skills in scaling, model monitoring, and deployment. Prerequisites Come with your questions Have a pen and paper handy to capture notes, insights, and inspiration
    Note: Online resource; Title from title screen (viewed March 17, 2021) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Inżynieria danych na platformie AWS : jak tworzyć kompletne potoki uczenia maszynowego (2021)
    Gliwice : Helion
    Details
    ISBN: 9788328391291 , 8328391295
    Language: Polish
    Pages: 1 online resource (472 pages) , illustrations
    Edition: [First edition].
    Uniform Title: Data science on AWS
    DDC: 006.3
    Keywords: Amazon Web Services (Firm) ; Machine learning ; Cloud computing ; Data mining ; Business Data processing ; Management Data processing
    Abstract: Platforma Amazon Web Services jest uważana za największą i najbardziej dojrzałą chmurę obliczeniową. Zapewnia bogaty zestaw specjalistycznych narzędzi ułatwiających realizację projektów z zakresu inżynierii danych i uczenia maszynowego. W ten sposób inżynierowie danych, architekci i menedżerowie mogą szybko zacząć używać danych do podejmowania kluczowych decyzji biznesowych. Uzyskanie optymalnej efektywności pracy takich projektów wymaga jednak dobrego rozeznania w możliwościach poszczególnych narzędzi, usług i bibliotek. Dzięki temu praktycznemu przewodnikowi szybko nauczysz się tworzyć i uruchamiać procesy w chmurze, a następnie integrować wyniki z aplikacjami. Zapoznasz się ze scenariuszami stosowania technik sztucznej inteligencji: przetwarzania języka naturalnego, rozpoznawania obrazów, wykrywania oszustw, wyszukiwania kognitywnego czy wykrywania anomalii w czasie rzeczywistym. Ponadto dowiesz się, jak łączyć cykle rozwoju modeli z pobieraniem i analizą danych w powtarzalnych potokach MLOps. W książce znajdziesz też zbiór technik zabezpieczania projektów i procesów z obszaru inżynierii danych, takich jak stosowanie usługi IAM, uwierzytelnianie, autoryzacja, izolacja sieci, szyfrowanie danych w spoczynku czy postkwantowe szyfrowanie sieci dla danych w tranzycie.
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    AI Superstream Series : Scaling AI (2021)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 3 hr., 14 min.)
    Edition: 1st edition
    Keywords: Electronic videos
    Abstract: Sponsored by intel and LSEG LABS Scaling AI is a notoriously difficult challenge. But it’s easier when you see what’s worked for others—and what hasn’t. This half-day virtual event brings together AI and machine learning engineers from across industries to show how they approach scaling at every stage of the project lifecycle. About the AI Superstream Series: This four-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 what scaling AI does (and doesn’t) include See what scaling AI might look like from design through deployment Explore what current AI leaders are achieving through scaling Discover real-world technical applications This recording of a live event is for you because… You're a machine learning engineer or data scientist interested in the challenges and benefits of scaling. You’re responsible for scaling your organization's machine learning and are looking for hands-on examples. You're wondering how to improve your own AI and machine learning. Recommended follow-up: Read AI and Analytics at Scale (report) Watch Meet the Expert: Dean Wampler on Scaling ML/AI Applications with Ray (recorded event)
    Note: Online resource; Title from title screen (viewed September 22, 2021) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    O’Reilly Radar : Data & AI (2021)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 6 hr., 52 min.)
    Edition: 1st edition
    Keywords: Electronic videos
    Abstract: O’Reilly Radar: Data & AI will showcase what’s new, what’s important, and what’s coming in the field. It includes two keynotes and two concurrent three-hour tracks—designed to lay out for tech leaders the issues, tools, and best practices that are critical to an organization at any step of their data and AI journey. You’ll explore everything from prototyping and pipelines to deployment and DevOps to responsible and ethical AI.
    Note: Online resource; Title from title screen (viewed October 14, 2021) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
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  • 5
    Online Resource
    Online Resource
    Data Science mit AWS : end-to-end pipelines für continuous machine learning implementieren (2022)
    Heidelberg : dpunkt
    Details
    Language: German
    Pages: 1 online resource (550 pages) , illustrations
    Edition: 1. Auflage.
    Uniform Title: Data science on AWS
    DDC: 006.3/1
    Keywords: Amazon Web Services (Firm) ; Machine learning ; Cloud computing ; Web services ; Artificial intelligence ; Artificial Intelligence ; Apprentissage automatique ; Infonuagique ; Services Web ; Intelligence artificielle ; artificial intelligence ; Electronic books
    Abstract: Mit diesem Buch lernen Machine-Learning- und KI-Praktiker, wie sie erfolgreich Data-Science-Projekte mit Amazon Web Services erstellen und in den produktiven Einsatz bringen. Es bietet einen detaillierten Einblick in den KI- und Machine-Learning-Stack von Amazon, der Data Science, Data Engineering und Anwendungsentwicklung vereint. Chris Fregly und Antje Barth beschreiben verständlich und umfassend, wie Sie das breite Spektrum an AWS-Tools nutzbringend für Ihre ML-Projekte einsetzen. Der praxisorientierte Leitfaden zeigt Ihnen konkret, wie Sie ML-Pipelines in der Cloud erstellen und die Ergebnisse dann innerhalb von Minuten in Anwendungen integrieren. Sie erfahren, wie Sie alle Teilschritte eines Workflows zu einer wiederverwendbaren MLOps-Pipeline bündeln, und Sie lernen zahlreiche reale Use Cases zum Beispiel aus den Bereichen Natural Language Processing, Computer Vision oder Betrugserkennung kennen. Im gesamten Buch wird zudem erläutert, wie Sie Kosten senken und die Performance Ihrer Anwendungen optimieren können.
    Note: Includes bibliographical references and index
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 6
    Online Resource
    Online Resource
    Data Science on AWS (2021)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (122 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: If you use data to make critical business decisions, this book is for you. Whether you're a data analyst, research scientist, data engineer, ML engineer, data scientist, application developer, or systems developer, this guide helps you broaden your understanding of the modern data science stack, create your own machine learning pipelines, and deploy them to applications at production scale. The AWS data science stack unifies data science, data engineering, and application development to help you level up your skills beyond your current role. Authors Antje Barth and Chris Fregly show you how to build your own ML pipelines from existing APIs, submit them to the cloud, and integrate results into your application in minutes instead of days. Innovate quickly and save money with AWS's on-demand, serverless, and cloud-managed services Implement open source technologies such as Kubeflow, Kubernetes, TensorFlow, and Apache Spark on AWS Build and deploy an end-to-end, continuous ML pipeline with the AWS data science stack Perform advanced analytics on at-rest and streaming data with AWS and Spark Integrate streaming data into your ML pipeline for continuous delivery of ML models using AWS and Apache Kafka
    Note: Online resource; Title from title page (viewed July 25, 2021) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 7
    Online Resource
    Online Resource
    GENERATIVE AI ON AWS : building context-aware multimodal reasoning applications (2023)
    [Sebastopol, California] : O'Reilly Media, Inc.
    Details
    ISBN: 9781098159191 , 1098159195
    Language: English
    Pages: 1 online resource
    Edition: First edtion.
    Parallel Title: Erscheint auch als
    DDC: 006.3
    Keywords: Amazon Web Services (Firm) ; Artificial intelligence Computer programs ; Application software
    Abstract: Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Apply generative AI to your business use cases Determine which generative AI models are best suited to your task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA) Align generative AI models to human values with reinforcement learning from human feedback (RLHF) Augment your model with retrieval-augmented generation (RAG) Explore libraries such as LangChain and ReAct to develop agents and actions Build generative AI applications with Amazon Bedrock.
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 8
    Online Resource
    Online Resource
    Jissen AWS dēta saiensu : endo tsū endo no MLOps paipurain jissō (2021)
    Tōkyō-to Shinjuku-ku : Orairī Japan
    Details
    Orig.schr. Ausgabe: 初版.
    Title: 実践AWSデータサイエンス : : エンドツーエンドのMLOpsパイプライン実装 /
    Publisher: オライリー・ジャパン,
    ISBN: 9784873119687 , 4873119685
    Language: Japanese
    Pages: 1 online resource (570 pages)
    Edition: Shohan.
    Uniform Title: Data science on AWS
    DDC: 004.6782
    Keywords: Amazon Web Services (Firm) ; Machine learning ; Cloud computing ; Data mining ; Business Data processing ; Management Data processing ; Amazon Web Services (Firm) ; Business ; Data processing ; Cloud computing ; Data mining ; Machine learning ; Management ; Data processing
    Note: In Japanese.
    URL: lizenzpflichtig
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