Experiment Tracking & Model Registries
From a folder of notebooks nobody trusts to a system of record for what's actually in production
Learn how experiment tracking captures runs, hyperparameters, metrics, and artifacts in a tool-agnostic pattern (the MLflow/Weights & Biases model), how a model registry becomes the single source of truth for what's actually deployed, how stage transitions (staging to production to archived) should be gated and by whom, and how to trace a production prediction back through the exact model version, training data snapshot, and code commit that produced it.
Practice questions (6)
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Reconstruct 'What's Actually in Production' After a Bucket-of-Files Incident
Intermediate · Free -
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Answer a Regulatory Lineage Request Under Time Pressure
Intermediate -
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Design a Promotion Gating Policy Across Model Risk Tiers
Intermediate -
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Use Tracked Runs to Choose Among a Hyperparameter Sweep
Intermediate -
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Compare Rollback Speed With and Without a Registry
Intermediate