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

How to package ML models

ML models can be packaged in various ways depending on business and tech requirements and as per operations for ML. ML models can be packaged and shipped in three ways, as discussed in the following sub-sections.

Serialized files

Serialization is a vital process for packaging an ML model as it enables model portability, interoperability, and model inference. Serialization is the method of converting an object or a data structure (for example, variables, arrays, and tuples) into a storable artefact, for example, into a file or a memory buffer that can be transported or transmitted (across computer networks). The main purpose of serialization is to reconstruct the serialized file into its previous data structure (for example, a serialized file into an ML model variable) in a different environment. This way, a newly trained ML model can be serialized into a file and exported into a new environment where it can de-serialized back into an ML model variable...