Kubeflow
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Kubeflow For Machine Learning
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Author : Trevor Grant
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-10-13
Kubeflow For Machine Learning written by Trevor Grant and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-13 with Computers categories.
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 Understand the differences between Kubeflow on different cluster types Train models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache Spark Keep your model up to date with Kubeflow Pipelines Understand how to capture model training metadata Explore how to extend Kubeflow with additional open source tools Use hyperparameter tuning for training Learn how to serve your model in production
Kubeflow Operations Guide
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Author : Josh Patterson
language : en
Publisher: O'Reilly Media
Release Date : 2020-12-04
Kubeflow Operations Guide written by Josh Patterson and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-04 with Computers categories.
Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable. Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft. Dive into Kubeflow architecture and learn best practices for using the platform Understand the process of planning your Kubeflow deployment Install Kubeflow on an existing on-premises Kubernetes cluster Deploy Kubeflow on Google Cloud Platform step-by-step from the command line Use the managed Amazon Elastic Kubernetes Service (EKS) to deploy Kubeflow on AWS Deploy and manage Kubeflow across a network of Azure cloud data centers around the world Use KFServing to develop and deploy machine learning models
Kubeflow For Machine Learning
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Author : L. Trevor Grant
language : en
Publisher:
Release Date : 2021
Kubeflow For Machine Learning written by L. Trevor Grant and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Machine learning categories.
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 Understand the differences between Kubeflow on different cluster types Train models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache Spark Keep your model up to date with Kubeflow Pipelines Understand how to capture model training metadata Explore how to extend Kubeflow with additional open source tools Use hyperparameter tuning for training Learn how to serve your model in production.
Kubeflow Operations Guide
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Author : Josh Patterson
language : en
Publisher: O'Reilly Media
Release Date : 2020-11-10
Kubeflow Operations Guide written by Josh Patterson and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-10 with Computers categories.
When deploying machine learning applications, building models is only a small part of the story. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. With this practical guide, data scientists, data engineers, and platform architects will learn how to plan and execute a Kubeflow project that can support workflows from on-premises to the cloud. Kubeflow is an open source Kubernetes-native platform based on Google's internal machine learning pipelines, and yet major cloud vendors including AWS and Azure advocate the use of Kubernetes and Kubeflow to manage containers and machine learning infrastructure. In today's cloud-based world, this book is ideal for any team planning to build machine learning applications. With this book, you will: Get a concise overview of Kubernetes and Kubeflow Learn how to plan and build a Kubeflow installation Operate, monitor, and automate your installation Provide your Kubeflow installation with adequate security Serve machine learning models on Kubeflow
Kubeflow For Machine Learning
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Author : Holden Karau
language : en
Publisher: O'Reilly Media
Release Date : 2020-12-08
Kubeflow For Machine Learning written by Holden Karau and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-08 with categories.
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
Machine Learning Engineering On Aws
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Author : Joshua Arvin Lat
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-10-27
Machine Learning Engineering On Aws written by Joshua Arvin Lat and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-27 with Computers categories.
Work seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cycle Key FeaturesGain practical knowledge of managing ML workloads on AWS using Amazon SageMaker, Amazon EKS, and moreUse container and serverless services to solve a variety of ML engineering requirementsDesign, build, and secure automated MLOps pipelines and workflows on AWSBook Description There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production. This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you'll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You'll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detail as you explore best practices when using each AWS. By the end of this AWS book, you'll be able to build, scale, and secure your own ML systems and pipelines, which will give you the experience and confidence needed to architect custom solutions using a variety of AWS services for ML engineering requirements. What you will learnFind out how to train and deploy TensorFlow and PyTorch models on AWSUse containers and serverless services for ML engineering requirementsDiscover how to set up a serverless data warehouse and data lake on AWSBuild automated end-to-end MLOps pipelines using a variety of servicesUse AWS Glue DataBrew and SageMaker Data Wrangler for data engineeringExplore different solutions for deploying deep learning models on AWSApply cost optimization techniques to ML environments and systemsPreserve data privacy and model privacy using a variety of techniquesWho this book is for This book is for machine learning engineers, data scientists, and AWS cloud engineers interested in working on production data engineering, machine learning engineering, and MLOps requirements using a variety of AWS services such as Amazon EC2, Amazon Elastic Kubernetes Service (EKS), Amazon SageMaker, AWS Glue, Amazon Redshift, AWS Lake Formation, and AWS Lambda -- all you need is an AWS account to get started. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.
Machine Learning Production Systems
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Author : Robert Crowe
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-10-02
Machine Learning Production Systems written by Robert Crowe and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-02 with Computers categories.
Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting—especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field. Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle. This book provides four in-depth sections that cover all aspects of machine learning engineering: Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storage Modeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture search Deployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and logging Productionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines
Kubeflow In Action
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Author : Juana Nakfour
language : en
Publisher: Manning
Release Date : 2022-03-29
Kubeflow In Action written by Juana Nakfour and has been published by Manning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-29 with Computers categories.
Kubeflow simplifies and automates machine learning tasks like interactive analysis, complex pipelines, and model training. Seamlessly push models to production in the containerized and distributed environment and scale your ML infrastructure from your laptop to a Kubernetes cluster. Kubeflow in Action shows you how to utilize Kubeflow to rapidly scale machine learning projects from a laptop to a distributed cluster. You’ll kick off with a rapid introduction to containers, benefit from careful guidance on Kubeflow’s installation and initial setup, and master core Kubeflow tasks like storing data, training models, and monitoring metrics. Detailed use cases help show how to construct complex pipelines, automate hyperparameter tuning, and implement network architecture search. You’ll quickly progress to a deep dive into Kubeflow’s more advanced uses, including training distributed models, deployment, A/B testing, and infrastructure monitoring to help trigger actions based on incoming data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Building Machine Learning Pipelines
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Author : Hannes Hapke
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-07-13
Building Machine Learning Pipelines written by Hannes Hapke and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-13 with Computers categories.
Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques
Kubeflow Operations And Workflow Engineering
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-12
Kubeflow Operations And Workflow Engineering written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-12 with Computers categories.
"Kubeflow Operations and Workflow Engineering" Unlock the full potential of machine learning at scale with "Kubeflow Operations and Workflow Engineering". This comprehensive guide provides a deep dive into the architecture, pipeline design, deployment patterns, and operational best practices behind Kubeflow—an industry-standard platform for orchestrating complex AI workflows on Kubernetes. Readers will explore Kubeflow’s modular microservices, core capabilities, and advanced orchestration paradigms, empowering them to design, deploy, and manage reliable machine learning solutions for enterprise environments. The book takes practitioners from foundational concepts through to specialized topics such as pipeline engineering, production-grade deployment, workflow scheduling, and resource optimization. Through detailed explorations of topics like component interoperability, state management, dynamic pipelines, distributed model training, and integration patterns, readers will learn proven methods to build robust, scalable, and secure MLOps infrastructures. Chapters on security, compliance, observability, and resilience address the demands of modern production environments and highly regulated industries, with guidance on access management, logging, policy enforcement, and high-availability design. Moving beyond the fundamentals, real-world case studies and emerging trends illuminate how leading organizations operationalize Kubeflow at scale, navigate hybrid and edge deployments, and integrate with modern tools and frameworks. Whether implementing federated learning, event-driven pipelines, or large language models, this book equips AI engineers, architects, and DevOps professionals with the practical knowledge to innovate and lead in the evolving MLOps landscape, leveraging Kubeflow as a strategic foundation for enterprise machine learning success.