Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale

by Emmanuel Raj

Get up and running with machine learning life cycle management and implement MLOps in your organization Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production. The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you'll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You'll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. 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. This MLOps book is for data scientists, software engineers, DevOps engineers, machine learning engineers, and business and technology leaders who want to build, deploy, and maintain ML systems in production using MLOps principles and techniques. Basic knowledge of machine learning is necessary to get started with this book.

370 pages, Paperback

Published April 19, 2021

Reviews

[Brian · Rating 3 out of 5]

This is an interesting field for sure, but was a little disappointed. It felt more like a MOOC than a book, and not in a good way (I didn't walk away with a much better understanding of the field). Topics are presented at a high level, which might be helpful for getting a general idea of things but not for learning any particular area very well. Still, since I didn't implement the project myself, I rounded up my rating. Maybe others who did won't share my opinion.

The epub format is yet again disappointing—code formatting is quite ugly (as with all programming books I've read in this format), and major sections within a chapter are marked in the table of contents as chapters themselves. Not to mention, there were several typos and grammatical errors (e.g., Apache Flint) that made me question how thoroughly the book had been reviewed before publishing.