Every trained model comes from a specific set of hyperparameters, code and data.
Without a tracking system, teams keep that history in filenames like
model_v2_final_FINAL.pkl — a pile of clues instead of a record. MLflow's
Tracking component logs each training run automatically: its
parameters, metrics, artifacts and the exact code version, all queryable later.
learning_rate and max_depth hyperparameters.mlflow.log_metric().MLflow's tracking store can hold thousands of runs per experiment, each with its own parameters, metrics, tags and artifact files — letting you query "show me every run where accuracy beat 0.9" instead of opening folders by hand.
Log hyperparameter sweeps as tracked runs on a 3D grid, and see how that compares to a pile of hand-named, hard-to-trace model files.
Each logged run is placed by its learning rate and tree depth, with height and colour encoding accuracy — mirroring how MLflow records parameters and metrics for every training run automatically.
Pick a learning rate and tree depth, then log the run to grow a bar on the tracking grid. Switch the colour metric, watch the gold beam track the best run, and compare it to the untracked file pile.
MLflow's tracking store can hold thousands of runs per experiment — each queryable by parameter or metric — replacing folders full of ambiguously named files like model_v2_final_FINAL.pkl.