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

Continuous integration, delivery, and deployment in MLOps

Automation is the primary reason for CI/CD in the MLOps workflow. The goal of enabling continuous delivery to the ML service is to maintain data and source code versions of the models, enable triggers to perform necessary jobs in parallel, build artifacts, and release deployments for production. Several cloud vendors are promoting DevOps services to monitor ML services and models in production, as well as orchestrate with other services on the cloud. Using CI and CD, we can enable continual learning, which is critical for a ML system's success. Without continual learning, a ML system is deemed to end up as a failed Proof of Concept (PoC).

Only a model deployed with continual learning capabilities can bring business value.

In order to learn to deploy a model in production with continual learning capabilities, we will explore CI, CD, and continuous delivery methods.

As you can see in Figure 7.1, CI is key to CD...