Set up rigorous experiment tracking, versioning, and reproducibility so every ML result can be reproduced exactly.
## CONTEXT The single most embarrassing moment in ML is being unable to reproduce your own best result. In 2026, reproducibility is the foundation of trustworthy ML, requiring versioned code, data, environments, and hyperparameters, plus a tracking system that logs every run. Tools like MLflow, Weights & Biases, and…
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