Experiment Tracking Logs
Project phase: Machine Learning Model Engineering
You keep a systematic record of every modelling experiment your team ran: the dataset version and split, the algorithm and hyperparameters, the code or commit it came from, and the resulting scores, in a consistent, machine-readable form so any run can be reproduced or fairly compared. You log failed and abandoned experiments too, and add a short summary tracing the line of reasoning through the experiments so your final model choice is fully supported by the tracked history.
Starting Points
Key Points
- Transparency: All experiments are recorded in a machine-readable format that others can easily read and reuse.
- Reproducibility: Every log entry contains enough detail (code version, data split, parameters) so that a teammate could recreate the exact same result.
- Validation: The logs show a clear progression from initial tests to a validated final model.
- Completeness: Both successful and failed experiments are documented to show the full scope of your research.
- Reasoning: A summary is included that connects the dots between different experiment runs.