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

Monitoring in the MLOps workflow

We learned about the MLOps workflow in Chapter 1, Fundamentals of MLOps Workflow. As shown in the following diagram, the monitoring block is an integral part of the MLOps workflow for evaluating the ML models' performance in production and measuring the ML system's business value. We can only do both (measure the performance and business value that's been generated by the ML model) if we understand the model's decisions in terms of transparency and explainability (to explain the decisions to stakeholders and customers).

Explainable Monitoring enables both transparency and explainability to govern ML systems in order to drive the best business value:

Figure 11.4 – MLOps workflow – Monitor

In practice, Explainable Monitoring enables us to monitor, analyze, and govern ML system, and it works in a continuous loop with other components in the MLOps workflow. It also empowers humans to engage...