1 · Model Tracking & Registries

1. Experiment Tracking & Model Registries

Version controlling weights, datasets, hyperparameters, and production staging environments.

9 min read · 2 MCQs

The Reproducibility Triad

Unlike traditional code where commits yield deterministic binaries, machine learning outcomes depend on Code Commit + Dataset Version + Hyperparameters/Hardware. MLflow and DVC track these dependencies as unified immutable runs.

Model Registry Stages

Promote serialized model artifacts through validated states (`Staging` -> `Production` -> `Archived`). A model registry governs CI/CD release gating, signature validation, and rollback metadata.

Chapter quiz

2 questions · pass mark 75%

1. What three elements are required to guarantee ML experiment reproducibility?
2. What is the primary role of an ML Model Registry?