Experiment Tracking: MLflow & Weights & Biases
A spreadsheet of hyperparameters is not an experiment record. A tracked run must capture enough to re-execute and audit it: config + code commit + immutable data version + metric curves + artifacts + lineage + cost. This topic covers what to record, how MLflow and W&B implement it (including the 2025 OpenTelemetry GenAI convergence), and how trackers anchor registry promotion and audit.
01.The Problem: Three Months Later, Nobody Can Find "The Best Model"
A team trains 300 model variants in a quarter. Then the questions start:
- Which one is deployed?
- Which learning rate made it good — 3e-4 or 5e-4?
- What data was it trained on... before the ETL job overwrote the table?
- Can we retrain it exactly if the server dies tonight?
If the answers live in notebooks, spreadsheets, and Slack threads, they are tribal knowledge — which means they are gone when the person is. A spreadsheet of hyperparameters is not an experiment record: it cannot tell you what actually ran.
So the question becomes
What would a record have to contain for a stranger to re-execute, verify, and audit any training run from scratch?
That record is what an experiment tracker produces.
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