Preface

MLOps is a systematic approach to building, deploying, and monitoring machine learning (ML) solutions. It is an engineering discipline that can be applied to various industries and use cases. This book presents comprehensive insights into MLOps coupled with real-world examples to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production.

You will begin by familiarizing yourself with MLOps workflow and start writing programs to train ML models. You’ll then move on to explore options for serializing and packaging ML models post-training to deploy them in production to facilitate machine learning inference. Next, you will learn about monitoring ML models and system performance using an explainable monitoring framework. Finally, you’ll apply the knowledge you’ve gained to build real-world projects.

By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization.