MLOps with Red Hat OpenShift : A Cloud-Native Approach to Machine Learning Operations / Ross Brigoli and Faisal Masood.

Format:

Book

Author/Creator:

Brigoli, Ross, author.

Masood, Faisal, author.

Language:

English

Subjects (All):

Machine learning.

Computer software--Development.

Computer software.

Physical Description:

1 online resource (238 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing, [2024]

Biography/History:

Brigoli Ross: Ross Brigoli is a consulting architect at Red Hat, where he focuses on designing and delivering solutions around microservices architecture, DevOps, and MLOps with Red Hat OpenShift for various industries. He has two decades of experience in software development and architecture. Masood Faisal: Faisal Masood is a cloud transformation architect at AWS. Faisal's focus is to assist customers in refining and executing strategic business goals. Faisal main interests are evolutionary architectures, software development, ML lifecycle, CD and IaC. Faisal has over two decades of experience in software architecture and development.

Summary:

Build and manage MLOps pipelines with this practical guide to using Red Hat OpenShift Data Science, unleashing the power of machine learning workflows Key Features 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 Book 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. 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 Who this book is 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.

Contents:

Cover

Title Page

Copyright and Credits

Contributors

Table of Contents

Preface

Part 1: Introduction

Chapter 1: Introduction to MLOps and OpenShift

What is MLOps?

Introduction to OpenShift

OpenShift features

Understanding operators

Understanding how OpenShift supports MLOps

Red Hat OpenShift Data Science (RHODS)

The advantages of the cloud

ROSA

Summary

References

Part 2: Provisioning and Configuration

Chapter 2: Provisioning an MLOps Platform in the Cloud

Technical requirements

Installing OpenShift on AWS

Preparing AWS accounts and service quotas

Preparing AWS for ROSA provisioning

Installing ROSA

Adding a new machine pool to the cluster

Installing Red Hat ODS

Installing partner software on RedHat ODS

Installing Pachyderm

Chapter 3: Building Machine Learning Models with OpenShift

Using Jupyter Notebooks in OpenShift

Provisioning an S3 store

Using ML frameworks in OpenShift

Using GPU acceleration for model training

Enabling GPU support

Building custom notebooks

Creating a custom notebook image

Importing notebook images

Part 3: Operating ML Workloads

Chapter 4: Managing a Model Training Workflow

Configuring Pachyderm

Versioning your data with Pachyderm

Training a model using Red Hat ODS

Building a model training pipeline

Installing Red Hat OpenShift Pipelines

Attaching a pipeline server to your project

Building a basic data science pipeline

Chapter 5: Deploying ML Models as a Service

Packaging and deploying models as a service

Saving and uploading models to S3

Updating the pipeline via model upload to S3

Creating a model server for Seldon

Deploying and accessing your model

Autoscaling the deployed models.

Releasing new versions of the model

Automating the model deployment process

Rolling back model deployments

Canary model deployment

Securing model endpoints

Chapter 6: Operating ML Workloads

Monitoring ML models

Installing and configuring Prometheus and Grafana

Logging inference calls

Optimizing cost

Chapter 7: Building a Face Detector Using the Red Hat ML Platform

Architecting a human face detector system

Training a model for face detection

Deploying the model

Validating the deployed model

Installing Redis on Red Hat OpenShift

Building and deploying the inferencing application

Bringing it all together

Optimizing cost for your ML platform

Machine management in OpenShift

Spot Instances

Index

Other Books You May Enjoy.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781805125853

1805125850

OCLC:

1420626952