Workflow✓ Mathematical
◆ The PatternLogging every run so you never lose a good result
Experiment tracking logs hyperparameters, metrics, artifacts, and code versions for every training run. Three months from now, when someone asks "which model was that?" you can answer. MLflow, Weights & Biases, and Neptune are the main tools.
// Interactive — experiment comparison table
# Python — MLflow experiment tracking import mlflow mlflow.set_experiment("fraud-detection") with mlflow.start_run(): mlflow.log_param("n_estimators", 200) mlflow.log_param("max_depth", 8) mlflow.log_metric("auc", 0.942) mlflow.log_metric("f1", 0.873) mlflow.sklearn.log_model(model, "model")
Pattern bridge: Experiment tracking is the ML version of a trading journal — systematic logging that turns isolated attempts into cumulative learning. The comparing runs framework from The Toolkit gives you the statistical tests to compare tracked experiments.