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

10 principles of source code management for ML

Here are 10 principles that can be applied to your code to ensure the quality, robustness, and scalability of your code:

  • Modularity: It is better to have modular code than to have one big chunk. Modularity encourages reusability and facilitates upgrading by replacing the required components. To avoid needless complexity and repetition, follow this golden rule:

    Two or more ML components should be paired only when one of them uses the other. If none of them uses each other, then pairing should be avoided.

    An ML component that is not tightly paired with its environment can be more easily modified or replaced than a tightly paired component.

  • Single task dedicated functions: Functions are important building blocks of pipelines and the system, and they are small sections of code that are used to perform particular tasks. The purpose of functions is to avoid repetition of commands and enable reusable code. They can easily become a complex...