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  • 1
    Online Resource
    Online Resource
    Kubeflow for Machine Learning (2020)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (130 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable. Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. Understand Kubeflow's design, core components, and the problems it solves Learn how to set up Kubeflow on a cloud provider or on an in-house cluster Train models using Kubeflow with popular tools including scikit-learn, TensorFlow, and Apache Spark Learn how to add custom stages such as serving and prediction Keep your model up-to-date with Kubeflow Pipelines Understand how to validate machine learning pipelines
    Note: Online resource; Title from title page (viewed November 25, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 2
    Online Resource
    Online Resource
    Serving machine learning models : a guide to architecture, stream processing engines, and frameworks (2017)
    Sebastopol, CA : O'Reilly Media
    Details
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: First edition.
    Keywords: Machine learning ; Artificial intelligence ; Business enterprises ; Technological innovations ; Electronic books ; Electronic books ; local
    Abstract: Model serving is a critical but often underappreciated aspect of machine learning.Once you have built a model using your training data set, you need to packageand deploy (i.e., serve) it. It's a surprisingly complex task, in part because modeltraining is usually handled by data scientists, and model serving is the domain ofsoftware engineers. These two groups have different functions, concerns, andtools, so the handoff can be tricky. Plus, machine learning is a hot and fast-growing field, spawning a slew of new tools that require software engineers tocreate new model serving frameworks. This book delves into the theory and practice of serving machine learning modelsin streaming applications. It proposes an overall architecture that implementscontrolled streams of both data and models that enables not only real-time modelserving, as part of processing input streams, but also real-time model updating. Italso covers: Step-by- step options for exporting models in tensorflow and PMMLformats. Implementation of model serving leveraging stream processing enginesand frameworks including Apache Flink, Apache Spark streaming, ApacheBeam, Apache Kafka streams, and Akka streams. Monitoring approaches for model serving implementations.
    Note: Description based on online resource; title from title page (Safari, viewed January 10, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781492024095/?ar
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Scaling Python with Ray (2023)
    [Erscheinungsort nicht ermittelbar] : O'Reilly Media, Inc. | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (38 pages)
    Edition: 1st edition
    Keywords: Electronic books
    Abstract: Serverless computing enables developers to concentrate solely on their applications rather than worry about where they've been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators. In this book, authors Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while avoiding single points of failure and manual scheduling. If your data processing has grown beyond what a single computer can handle, this book is for you. Written by experienced software architecture practitioners, Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness. This book covers distributed processing (the pure Python implementation of serverless) and shows you how to: Implement stateful applications with Ray actors Build workflow management in Ray Use Ray as a unified platform for batch and streaming Implement advanced data processing with Ray Apply microservices with Ray platform Implement reliable Ray applications
    Note: Online resource; Title from title page (viewed June 25, 2023) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 4
    Online Resource
    Online Resource
    Professional Hadoop solutions (2013)
    [Erscheinungsort nicht ermittelbar] : Wrox
    Details
    ISBN: 1118611934 , 9781118611937
    Language: English
    Pages: 1 online resource (xxiii, 477 p.) , ill.
    Series Statement: Wrox Programmer to programmer
    Parallel Title: Erscheint auch als
    Keywords: Apache Hadoop ; File organization (Computer science) ; Electronic data processing ; Distributed processing ; Big data ; Electronic books ; Electronic books ; local
    Abstract: The go-to guidebook for deploying Big Data solutions with Hadoop Today's enterprise architects need to understand how the Hadoop frameworks and APIs fit together, and how they can be integrated to deliver real-world solutions. This book is a practical, detailed guide to building and implementing those solutions, with code-level instruction in the popular Wrox tradition. It covers storing data with HDFS and Hbase, processing data with MapReduce, and automating data processing with Oozie. Hadoop security, running Hadoop with Amazon Web Services, best practices, and automating Hadoop processes in real time are also covered in depth. With in-depth code examples in Java and XML and the latest on recent additions to the Hadoop ecosystem, this complete resource also covers the use of APIs, exposing their inner workings and allowing architects and developers to better leverage and customize them. The ultimate guide for developers, designers, and architects who need to build and deploy Hadoop applications Covers storing and processing data with various technologies, automating data processing, Hadoop security, and delivering real-time solutions Includes detailed, real-world examples and code-level guidelines Explains when, why, and how to use these tools effectively Written by a team of Hadoop experts in the programmer-to-programmer Wrox style Professional Hadoop Solutions is the reference enterprise architects and developers need to maximize the power of Hadoop.
    Note: Description based on print version record
    URL: https://learning.oreilly.com/library/view/-/9781118824184/?ar
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    Online Resource
    MPI Ethno. Forsch.
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