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MLOps with Red Hat OpenShift
MLOps with Red Hat OpenShift

MLOps with Red Hat OpenShift: A cloud-native approach to machine learning operations

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Profile Icon Ross Brigoli Profile Icon Faisal Masood
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$9.99 $35.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (2 Ratings)
eBook Jan 2024 238 pages 1st Edition
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$9.99 $35.99
Paperback
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Arrow left icon
Profile Icon Ross Brigoli Profile Icon Faisal Masood
Arrow right icon
$9.99 $35.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (2 Ratings)
eBook Jan 2024 238 pages 1st Edition
eBook
$9.99 $35.99
Paperback
$44.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$9.99 $35.99
Paperback
$44.99
Subscription
Free Trial
Renews at $19.99p/m

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MLOps with Red Hat OpenShift

Part 1: Introduction

This part covers the basic concepts of MLOps and an introduction to Red Hat OpenShift.

This part has the following chapters:

  • Chapter 1, Introduction to MLOps and OpenShift
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Key benefits

  • Grasp MLOps and machine learning project lifecycle through concept introductions
  • Get hands on with provisioning and configuring Red Hat OpenShift Data Science
  • Explore model training, deployment, and MLOps pipeline building with step-by-step instructions
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

MLOps with OpenShift offers practical insights for implementing MLOps workflows on the dynamic OpenShift platform. As organizations worldwide seek to harness the power of machine learning operations, this book lays the foundation for your MLOps success. Starting with an exploration of key MLOps concepts, including data preparation, model training, and deployment, you’ll prepare to unleash OpenShift capabilities, kicking off with a primer on containers, pods, operators, and more. With the groundwork in place, you’ll be guided to MLOps workflows, uncovering the applications of popular machine learning frameworks for training and testing models on the platform. As you advance through the chapters, you’ll focus on the open-source data science and machine learning platform, Red Hat OpenShift Data Science, and its partner components, such as Pachyderm and Intel OpenVino, to understand their role in building and managing data pipelines, as well as deploying and monitoring machine learning models. Armed with this comprehensive knowledge, you’ll be able to implement MLOps workflows on the OpenShift platform proficiently.

Who is this book for?

This book is for MLOps and DevOps engineers, data architects, and data scientists interested in learning the OpenShift platform. Particularly, developers who want to learn MLOps and its components will find this book useful. Whether you’re a machine learning engineer or software developer, this book serves as an essential guide to building scalable and efficient machine learning workflows on the OpenShift platform.

What you will learn

  • Build a solid foundation in key MLOps concepts and best practices
  • Explore MLOps workflows, covering model development and training
  • Implement complete MLOps workflows on the Red Hat OpenShift platform
  • Build MLOps pipelines for automating model training and deployments
  • Discover model serving approaches using Seldon and Intel OpenVino
  • Get to grips with operating data science and machine learning workloads in OpenShift

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 31, 2024
Length: 238 pages
Edition : 1st
Language : English
ISBN-13 : 9781805125853
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Product Details

Publication date : Jan 31, 2024
Length: 238 pages
Edition : 1st
Language : English
ISBN-13 : 9781805125853
Category :
Tools :

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Table of Contents

12 Chapters
Part 1: Introduction Chevron down icon Chevron up icon
Chapter 1: Introduction to MLOps and OpenShift Chevron down icon Chevron up icon
Part 2: Provisioning and Configuration Chevron down icon Chevron up icon
Chapter 2: Provisioning an MLOps Platform in the Cloud Chevron down icon Chevron up icon
Chapter 3: Building Machine Learning Models with OpenShift Chevron down icon Chevron up icon
Part 3: Operating ML Workloads Chevron down icon Chevron up icon
Chapter 4: Managing a Model Training Workflow Chevron down icon Chevron up icon
Chapter 5: Deploying ML Models as a Service Chevron down icon Chevron up icon
Chapter 6: Operating ML Workloads Chevron down icon Chevron up icon
Chapter 7: Building a Face Detector Using the Red Hat ML Platform Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(2 Ratings)
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H2N Mar 11, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It is for MLOps engineers, DevOps engineers, IT architects, and data scientists interested in MLOps and the Red Hat OpenShift Data Science platform. It introduces MLOps concepts and the Red Hat OpenShift platform, walking readers through provisioning an MLOps platform in the cloud, building machine learning models with OpenShift, managing model training workflows, deploying ML models as a service, and operating ML workloads.
Amazon Verified review Amazon
Om S Mar 08, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
"MLOps with Red Hat OpenShift" serves as an essential tool for MLOps and DevOps engineers, data experts, and scientists keen on mastering machine learning operations (MLOps) using OpenShift. This book breaks down MLOps into digestible parts, from initial data setup to the final deployment of models, guiding readers through using OpenShift to create effective ML pipelines. It begins with fundamental MLOps concepts, preparing readers with a solid foundation and a clear understanding of OpenShift's basics, such as containers and pods.The journey continues as readers dive into MLOps workflows, exploring the integration of machine learning frameworks for model training and testing. The focus then shifts to the use of Red Hat OpenShift Data Science and partner components like Pachyderm and Intel OpenVino. These tools play critical roles in managing data pipelines and deploying and monitoring machine learning models.Step-by-step instructions ensure that readers can follow along with ease, making the complexities of MLOps workflows manageable and understandable. The book not only educates on building MLOps pipelines for automation but also delves into model serving techniques using advanced tools like Seldon and Intel OpenVino.Targeted at MLOps and DevOps engineers, data architects, and scientists interested in leveraging OpenShift for machine learning, this book is also an invaluable resource for developers eager to learn about MLOps components. It effectively demystifies the process of building scalable and efficient machine learning workflows on the OpenShift platform, offering readers the skills needed for MLOps success.
Amazon Verified review Amazon
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