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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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

€25.99 ~~€37.99~~

Azure Data Engineer Associate Certification Guide

€37.99

€33.99

Total € 97.97 109.97 12.00 saved

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H2N Mar 11, 2024

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

Om S Mar 08, 2024

"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.

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

What do you get with eBook?