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

Hands-on deployment (for the business problem)

In this section, we will learn how to deploy solutions for the business problem we have been working on. So far, we have done data processing, ML model training, serialized models, and registered them to the Azure ML workspace. In this section, we will explore how inference is performed on the serialized model on a container and an auto-scaling cluster. These deployments will give you a broad understanding and will prepare you well for your future assignments.

We will use Python as the primary programming language, and Docker and Kubernetes for building and deploying containers. We will start with deploying a REST API service on an Azure container instance using Azure ML. Next, we will deploy a REST API service on an auto-scaling cluster using Kubernetes (for container orchestration) using Azure ML, and lastly, we will deploy on an Azure container instance using MLflow and an open source ML framework; this way, we will learn how to...