Preface

Machine Learning (ML) is the new black. Organizations are investing in adopting and uplifting their ML capabilities to build new products and improve customer experience. The focus of this book is on assisting organizations and teams to get business value out of ML initiatives. By implementing MLOps with Kubernetes, data scientists, IT operations professionals, and data engineers will be able to collaborate and build ML solutions that create tangible outcomes for their business. This book enables teams to take a practical approach to work together to bring the software engineering discipline to the ML project life cycle.

You'll begin by understanding why MLOps is important and discover the different components of an ML project. Later in the book, you'll design and build a practical end-to-end MLOps project that'll use the most popular OSS components. As you progress, you'll get to grips with the basics of MLOps and the value it can bring to your ML projects, as well as gaining experience in building, configuring, and using an open source, containerized ML platform on Kubernetes. Finally, you'll learn how to prepare data, build and deploy models quickly, and automate tasks for an efficient ML pipeline using a common platform. The exercises in this book will help you get hands-on with using Kubernetes and integrating it with OSS, such as JupyterHub, MLflow, and Airflow.

By the end of this book, you'll have learned how to effectively build, train, and deploy an ML model using the ML platform you built.