01Module 05: CI/CD for MLBuild CI/CD pipelines that catch ML-specific failures - not just broken code, but broken models.02CI/CD for ML vs SoftwareUnderstand why standard software CI/CD is insufficient for ML and what additional stages you need to catch real failures.03Testing ML CodeBuild a practical ML test suite from zero - covering the full pyramid from unit tests through model validation without testing everything.04GitHub Actions for MLBuild a complete ML CI pipeline in GitHub Actions that triggers training only when training data or model code changes - not on every commit.05GitLab CI for MLBuild an enterprise-grade ML CI/CD pipeline in GitLab CI - from data commit to production deployment with DAG pipelines, GPU runners, and environments.06Model Evaluation GatesDesign automated model quality gates that block promotion when a model fails on demographic subgroups - not just on aggregate metrics.07Automated Retraining PipelinesBuild fully automated trigger-based model retraining pipelines - from drift detection through training to production deployment, with human-in-the-loop approval.08Continuous TrainingDesign continuous training systems that safely update models every few hours - covering CT maturity levels, warm-starting, failure modes, and monitoring.
