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

by Emmanuel Raj

Overview

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.

Product Details

ISBN-13:	9781800566323
Publisher:	Packt Publishing
Publication date:	04/19/2021
Sold by:	Barnes & Noble
Format:	eBook
Pages:	370
File size:	16 MB
Note:	This product may take a few minutes to download.

About the Author

Emmanuel Raj is a Finland-based Senior Machine Learning Engineer with 6+ years of industry experience. He is also a Machine Learning Engineer at TietoEvry and a Member of the European AI Alliance at the European Commission. He is passionate about democratizing AI and bringing research and academia to industry. He holds a Master of Engineering degree in Big Data Analytics from Arcada University of Applied Sciences. He has a keen interest in R&D in technologies such as Edge AI, Blockchain, NLP, MLOps and Robotics. He believes "the best way to learn is to teach", he is passionate about sharing and learning new technologies with others.

Table of Contents

Table of Contents

1. Fundamentals of MLOps Workflow
2. Characterizing your Machine learning problem
3. Code Meets Data
4. Machine Learning Pipelines
5. Model evaluation and packaging
6. Key principles for deploying your ML system
7. Building robust CI and CD pipelines
8. APIs and microservice Management
9. Testing and Securing Your ML Solution
10. Essentials of Production Release
11. Key principles for monitoring your ML system
12. Model Serving and Monitoring
13. Governing the ML system for Continual Learning