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

Summary

Congratulations! You have just experienced building an end-to-end MLOps workflow from scratch. You have trained and deployed an ML model and built a pipeline to automate your model training and deployment workflow using the tools that come with OpenShift Data Science. You have also successfully built a backend application that hosts your model and exposes it as an HTTP endpoint.

You have seen how OpenShift not only provides a full ML life cycle but also hosts your application and supports technologies such as Redis. All the components that have been deployed will benefit from the scalability of the platform.

Your journey does not stop here. The models we have shown here are just an example. You can deploy open source large language models (LLMs) on the platform.

Happy learning!