Engineering MLOps

Section 1: Framework for Building Machine Learning Models

This part will equip readers with the foundation of MLOps and workflows to characterize their ML problems to provide a clear roadmap for building robust and scalable ML pipelines. This will be done in a learn-by-doing approach via practical implementation using proposed methods and tools (Azure Machine Learning services or MLflow).

This section comprises the following chapters:

• Chapter 1, Fundamentals of MLOps WorkFlow
• Chapter 2, Characterizing Your Machine Learning Problem
• Chapter 3, Code Meets Data
• Chapter 4, Machine Learning Pipelines
• Chapter 5, Model Evaluation and Packaging