Deploying and Proving ML Value

In Part 3 of this book, we will provide a comprehensive guide to the complete, end-to-end life cycle of a machine learning (ML) system, moving from defining its purpose to proving its causal impact on the business. We will start with the foundational framework for success, moving beyond simple accuracy to define multi-dimensional, business-aligned goals, essential guardrail metrics to prevent unintended harm, and surrogate metrics to track long-term objectives. Next, we will dive into “productization,” which is the critical process of operationalizing models from experimental notebooks into robust, production-grade systems. We will discuss MLOps best practices, the importance of reproducible pipelines for code and data, and the architectural choices for model serving. Finally, we will explore the science of causal inference to answer the critical question, “Did the system do what it was intended to do?”, covering the “...