Book Image

Engineering MLOps

By : Emmanuel Raj
Book Image

Engineering MLOps

By: Emmanuel Raj

Overview of this book

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.
Table of Contents (18 chapters)
1
Section 1: Framework for Building Machine Learning Models
7
Section 2: Deploying Machine Learning Models at Scale
13
Section 3: Monitoring Machine Learning Models in Production

Summary

In this chapter, we have learned the key principles of testing and security by design. We explored the various methods to test ML solutions in order to secure them. For a comprehensive understanding and hands-on experience, implementation was done to load test our previously deployed ML model (from Chapter 7, Building Robust CI and CD Pipelines) to predict the weather. With this, you are ready to handle the diverse testing and security scenarios that will be channeled your way.

In the next chapter, we will delve into the secrets of deploying and maintaining robust ML services in production. This will enable you to deploy robust ML solutions in production. Let's delve into it.