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 learned about the key principles for monitoring an ML system. We explored some common monitoring methods and the Explainable Monitoring Framework (including the monitor, analyze, and govern stages). We then explored the concepts of Explainable Monitoring thoroughly.

In the next chapter, we will delve into a hands-on implementation of the Explainable Monitoring Framework. Using this, we will build a monitoring pipeline in order to continuously monitor the ML system in production for the business use case (predicting weather at the port of Turku).

The next chapter is quite hands-on, so buckle up and get ready!