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

In this chapter, you learned how to use the core features of RHODS. You learned how to create and manage data science projects, workbenches, storage, and data connections.

You also saw how RHODS does the heavy lifting for hardware and software provisioning for your model development workflow. This includes learning how to take advantage of GPUs through machine pools. This dynamic model development environment enables your team to be more agile and focus on model building instead of managing the libraries.

Finally, you learned how to extend the base images to create a set of environments that is more suited to your needs. There, you learned how to create and use custom notebook images in RHODS. This allows you to further customize and tailor the experiences of your data science team.

In the next chapter, you will learn how to build and package ML models for consumption.