Production ML Engineering: From Research Code to Reliable Systems

Turning experimental notebooks into resilient, testable, and observable services.

We cover the patterns and checklists used to ship ML systems: configuration-driven pipelines, reproducible data processing, temporal validation, model registries, and serving patterns with observability. Examples draw on the Energy Recommendation System and Heart Failure Readmission projects.

Key Practices

• Temporal evaluation and leakage prevention for time series.
• Typed interfaces between data, model, and serving layers.
• Monitoring: drift, data quality, latency, and error budgets.

Explore

• Energy project: Technical Deep-Dive
• Healthcare project: Technical Overview

Includes frameworks for production readiness reviews.