Build End-to-End Machine Learning Pipeline

Production machine learning is an engineering discipline, not a research activity — and the gap between a notebook that produces a good model and a system that reliably retrains, evaluates, and deploys updated models automatically is where most ML projects fail. This plan builds the infrastructure side of that gap: the pipeline that turns raw data into deployed predictions, with the monitoring to catch drift and the tooling to address it.

The session covers problem framing with the right success metrics (not just accuracy — precision, recall, and business-relevant measures), data preparation and feature engineering, training with proper evaluation methodology, building an automated pipeline with scheduling and triggers, and deploying with monitoring instrumentation. The emphasis on evaluation methodology before writing model code is intentional: many ML systems are built optimizing the wrong metric, and discovering that after deployment is expensive.

Model drift is the production problem that catches teams off guard most often. A model that performs well on historical training data gradually degrades as the world changes — not because code breaks, but because the statistical relationships it learned no longer hold in current data. The monitoring infrastructure built in this plan catches drift through input distribution monitoring and output quality metrics. An ML system you can trust differs from one you're constantly worrying about almost entirely because monitoring was built before launch, not planned for later.