ML Experiment Tracking Template

Tools like MLflow and Weights & Biases automatically track metrics, hyperparameters, and artifacts. An experiment log adds the reasoning layer: why you ran the experiment, what you expected, what you learned, and what it means for the next experiment. Use both. The tool captures data. The log captures decisions. The [AI PM Handbook](/ai-guide) covers how product managers should engage with ML experimentation workflows.

There is no fixed number, but convergence is the signal. If your last 5 experiments each improved the primary metric by less than 0.1%, you have likely reached diminishing returns for the current approach. Track enough experiments to identify the sensitivity of each hyperparameter, then move to a different lever (more data, better features, different architecture).

Yes. Product managers do not need to understand every hyperparameter, but they should read the Hypothesis, Analysis, and Comparison Table sections. These sections translate ML experiments into product decisions: is the model good enough to ship? What trade-offs are we making? Where is the model still weak?

Log them with the same detail as successful experiments. Failed experiments are often more informative than successful ones. Document what went wrong (diverged training, data issue, flawed hypothesis) and what you learned. Do not delete or hide failed experiments. They prevent other team members from repeating the same mistake.

Deploy when you meet your pre-defined success criteria AND the last 3-5 experiments show marginal gains. Perfectionism in ML experimentation delays value delivery. Use the [AI Eval Scorecard](/tools/ai-eval-scorecard) to determine whether the current best model meets your deployment threshold. Set a time-box for experimentation in the project plan. ---