Module 4 - Model Registry and Lifecycle
Without a model registry, your ML team is an archaeological dig. With one, it's an engineering discipline.
Every ML team eventually hits the same wall. Models proliferate. S3 buckets fill with files named model_v2_final_FINAL_USE_THIS.pkl. No one knows which model is in production, what data it was trained on, or whether it's safe to roll back. The 2am incident comes - and it's a disaster.
A model registry is the solution. It is the single source of truth for every model your organization has ever trained, evaluated, and deployed. It tracks not just the artifact itself but its full provenance: what data, what code version, what hyperparameters, what performance metrics.
This module covers everything from foundational concepts to production-grade governance.
What You Will Learn
Lessons in This Module
| # | Lesson | What You Learn |
|---|---|---|
| 01 | Model Registry Concepts | Lifecycle stages, metadata, lineage graphs, registry vs artifact store |
| 02 | MLflow Model Registry | Registering models, stages, aliases, webhooks, access control |
| 03 | Model Versioning Strategies | Semantic versioning, triggers, champion/challenger, deprecation |
| 04 | Model Artifacts and Formats | Pickle, joblib, ONNX, MLflow flavors, model signing |
| 05 | Model Lineage and Governance | End-to-end lineage, model cards, GDPR, audit trails |
| 06 | Model Deployment Patterns | Blue-green, canary, shadow mode, A/B testing, rollback |
Key Concepts at a Glance
| Concept | Definition |
|---|---|
| Model Registry | Centralized store tracking model versions, metadata, and lifecycle state |
| Model Lineage | Full provenance chain: data → features → model → predictions |
| Model Stage | Lifecycle state: None → Staging → Production → Archived |
| Model Alias | Human-readable pointer to a specific version (e.g., champion) |
| Model Card | Standardized documentation of model purpose, limits, and fairness |
| ONNX | Open format for portable model serialization across frameworks |
| Canary Deployment | Routing a small percentage of traffic to a new model version |
| Shadow Mode | Running a new model in parallel without serving its predictions |
Why This Module Matters
MLOps practitioners consistently cite model management as one of the hardest operational challenges in production ML. The model registry is not optional infrastructure - it is the difference between a team that can ship reliably and one that is always firefighting.
After this module you will be able to design a model registry workflow, implement it with MLflow, ensure models are traceable from training data to production inference, and deploy models safely using proven patterns.