Book Image

MLOps with Red Hat OpenShift

By : Ross Brigoli, Faisal Masood
Book Image

MLOps with Red Hat OpenShift

By: Ross Brigoli, Faisal Masood

Overview of this book

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.
Table of Contents (13 chapters)
Free Chapter
1
Part 1: Introduction
3
Part 2: Provisioning and Configuration
6
Part 3: Operating ML Workloads

Introduction to OpenShift

Although this book is not about operationalizing the OpenShift platform, a basic introduction to the platform is helpful. In this section, you will learn about the core concepts of Kubernetes and OpenShift.

OpenShift is a complete application platform based on Kubernetes. It is also categorized as Enterprise Kubernetes. Kubernetes provides a solid foundation for container hosting and orchestration. Moreover, Kubernetes provides core functionalities, such as cluster-state management, where a reconcile loop makes sure that the cluster state and the desired state are in sync. Kubernetes also includes a set of APIs to interact with the cluster. Kubernetes is a great platform, but in reality, applications need much more than just the core services provided by Kubernetes.

Assume that you want to deploy a Python application on a Kubernetes cluster. Let’s assess what is required. First, you need to package your application as a container image. Secondly...