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)
Section 1: Framework for Building Machine Learning Models
Section 2: Deploying Machine Learning Models at Scale
Section 3: Monitoring Machine Learning Models in Production

Understanding the need for continual learning

When we got started in Chapter 1, Fundamentals of MLOps Workflow, we learned about the reasons AI adoption is stunted in organizations. One of the reasons was the lack of continual learning in ML systems. Yes, continual learning! We will address this challenge in this chapter and make sure we learn how to enable this capability by the end of this chapter. Now, let's look into continual learning.

Continual learning

Continual learning is built on the principle of continuously learning from data, human experts, and the external environment. Continual learning enables lifelong learning, with adaptation at its core. It enables ML systems to become intelligent over time to adapt to the task at hand. It does this by monitoring and learning from the environment and the human experts assisting the ML system. Continual learning can be a powerful add-on to an ML system. It can allow you to realize the maximum potential of an AI system...