Preface

MLOps, or Machine Learning Operations, is all about streamlining and harmonizing the intricate dance between developing and deploying machine learning models. It’s like the conductor orchestrating a symphony, ensuring a seamless flow from the creative realm of data science to the robust reality of IT operations.

This book introduces a practical approach to implementing MLOps on the Red Hat OpenShift platform. It starts by presenting key MLOps concepts such as data preparation, model training, and packaging and deployment automation. An overview of OpenShift’s fundamental building blocks—deployments, pods, and operators—is then provided. Once the basics are covered, the book delves into platform provisioning and deepens our exploration of MLOps workflows.

Throughout the book, Red Hat OpenShift Data Science (RHODS), a data science platform designed to run on OpenShift, is utilized. You will experience creating ML projects, notebooks, and training and deployment pipelines using RHODS. The book also covers the use of partner software components that complement the RHODS platform, including Pachyderm and Intel OpenVino.

By the book’s end, you will gain a solid understanding of MLOps concepts, best practices, and the skills needed to implement MLOps workflows with Red Hat OpenShift Data Science on the Red Hat OpenShift platform.

Who this book is for

This book is for MLOps engineers, DevOps engineers, IT architects, and data scientists who want to gain an understanding of MLOps concepts and are interested in learning the Red Hat OpenShift Data Science platform. A basic understanding of OpenShift or Kubernetes would help in better understanding the inner workings of the exercises presented in this book. A basic knowledge of data science or machine learning and Python coding skills will also help you perform the data science parts of the exercises smoothly.

What this book covers

Chapter 1, Introduction to MLOps and OpenShift, starts with a brief introduction to MLOps and the basics of Red Hat OpenShift. The chapter then discusses how OpenShift enables machine learning projects and how Red Hat OpenShift Data Science and partner software products comprise a complete MLOPS platform.

Chapter 2, Provisioning an MLOps Platform in the Cloud, will walk you through provisioning Red Hat OpenShift, Red Hat OpenShift Data Science, and Pachyderm on the AWS cloud. The chapter contains step-by-step instructions on how to provision the base MLOps platform.

Chapter 3, Building Machine Learning Models with OpenShift, starts with the initial configurations of the platform components to prepare for model building. The chapter walks you through the configuration steps and ends with an introduction to the data science projects, workbenches, and the Jupyter Notebook.

Chapter 4, Managing a Model Training Workflow, digs deeper into the platform configuration covering OpenShift Pipelines for building model training pipelines and using Pachyderm for data versioning. By the end of the chapter, you will have built an ML model using a training pipeline you created.

Chapter 5, Deploying ML Models as a Service, introduces the model serving component of the platform. The chapter will walk you through how to enhance further the pipeline to automate the deployment of ML models.

Chapter 6, Operating ML Workloads, talks about the operational aspects of MLOps. The chapter focuses on logging and monitoring the deployed ML models and briefly discusses strategies for optimizing operational costs.

Chapter 7, Building a Face Detector Using the Red Hat ML Platform, walks you through the process of building a new AI-enabled application from end to end. The chapter helps you practice the knowledge and skills you gained in the previous chapters. The chapter also introduces Intel OpenVino as another option for model serving. By the end of this chapter, you will have built an AI-enabled web application running on OpenShift and used all of the Red Hat OpenShift Data Science features.

To get the most out of this book

You will need a basic knowledge of Kubernetes or OpenShift and basic Python coding skills on Jupyter Notebooks. Most activities are done using the web-based graphical user of Red Hat OpenShift and Red Hat OpenShift Data Science. However, specific steps require running Linux commands and interacting with the OpenShift API. Lastly, we recommend that you perform the exercises in this book to get a hands-on experience of the platform.

Software/hardware covered in the book	Operating system requirements
AWS CLI (aws)	Windows, macOS, or Linux
Red Hat OpenShift Client (oc)	Windows, macOS, or Linux

The software listed above must be installed on your local machine. These are used to interact with the platform from your client computer. The rest of the interaction with the platform is through the OpenShift web console and the Red Hat OpenShift Data Science web console.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/MLOps-with-Red-Hat-OpenShift

If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Create a user named admin.”

A block of code is set as follows:

storage:
backend: MINIO
minio:
bucket: pachyderm

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

curl -O -L https://mirror.openshift.com/pub/openshift-v4/client/rosa/latest/rosa-linux.tar.gz
tar -xvzf rosa-linux.tar
echo PATH=$PATH:/home/cloudshell-user >> ~/.bashrc

Any command-line input or output is written as follows:

echo <the rendered yaml string> | oc apply -f-

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “Click on the Increase service quotas button if applicable to your cluster.”

Tips or important notes

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Get in touch

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