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 continuous operations in MLOps, primarily, continuous integration, delivery, and deployment. We have learned this by performing a hands-on implementation of setting up a CI/CD pipeline and test environment using Azure DevOps. We have tested the pipeline for execution robustness and finally looked into some triggers to enhance the functionality of the pipeline and also set up a Git trigger for the test environment. This chapter serves as the foundation for continual operations in MLOps and equips you with the skills to automate the deployment pipelines of ML models for any given scenario on the cloud, with continual learning abilities in tune with your business.

In the next chapter, we will look into APIs, microservices, and what they have to offer for MLOps-based solutions.