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.
