Persist MLflow experiment parameters, metrics, artifacts, and nested run lineage.
npx skills add https://github.com/thistleknot/skills --skill mlflow-thistleknot
Please help me install this Agent Skill. Skill: mlflow Source: https://github.com/thistleknot/skills/tree/main/mlflow Command: npx skills add https://github.com/thistleknot/skills --skill mlflow-thistleknot
MLflow experiment tracking removes the confusion and irreproducibility that happen when tuning runs only store local results, making it hard to compare trials, reproduce outcomes, and inspect what actually produced a score.
Use case example: You run an Optuna nested-CV search over model and retrieval settings and need to later answer which hyperparameters won, why they won, and where the supporting artifacts (configs, plots, evaluation tables) are located.
Use mlflow to log every Optuna trial’s parameters and objective metrics into a named experiment, then attach the final chosen artifacts to the corresponding holdout run.
High-intent search queries and answers about installing and using this skill.
Track Optuna nested CV experiment parameters and metrics by logging each trial's tunable parameters, objective metrics, and artifacts into a named MLflow experiment. This creates a queryable ledger of campaigns, outer folds, and holdout verification runs for direct comparison.
Organize nested run lineage for model tuning by structuring MLflow runs into a consistent hierarchy of campaigns, outer folds, trials, and final training. This topology supports fast comparison and ties results to specific dataset versions and branches.
Run lineage supports model promotion after holdout verification by tying experiment metrics to dataset versions, branches, and commits. This disciplined tracking ensures only verified runs with persisted artifacts are promoted, maintaining full auditability.
Log artifacts and configs alongside scalar metrics during experiment tracking by persisting them to the corresponding MLflow run. This attaches supporting files like plots and evaluation tables directly to the holdout run for later inspection.
Disciplined experiment naming and tagging contracts for MLflow are required to maintain a queryable and comparable experiment ledger. Consistent tagging ensures you can later audit exactly which hyperparameters won and locate the supporting artifacts across days and agents.
MLflow experiment tracking works for agentic harness workflows by persisting parameters, metrics, artifacts, and nested run lineage across executions. This removes irreproducibility when local tuning runs make it hard to compare trials and inspect scores.
elijahmurrayConfigure GitHub Issues, Linear, Jira, or GitLab for issue tracking.
thbraetCompare MLflow experiment runs across metrics, hyperparameters, tags, and artifacts.
Yeachan-HeoAutomate ML evaluation with baselines, stratified cross-validation, and leakage checks.
Driving-TeacherFetch Hackle MCP experiment data, compare with snapshots, and share insights via Slack.
