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Learn AI with Python · Lesson

Experiment Tracking with MLflow

mlflow.start_run(), log_param(), log_metric(), log_artifact(), viewing the MLflow UI.

Why Track Experiments?

ML work means dozens of runs with different parameters and data. Without tracking you lose which settings produced your best model. MLflow records parameters, metrics, and artifacts for every run so results are reproducible and comparable.

Installing and Importing

MLflow is a single pip install and import.

# pip install mlflow
import mlflow
import mlflow.sklearn

All lessons in this course

  1. Experiment Tracking with MLflow
  2. Model Registry and Versioning
  3. Building Reproducible ML Pipelines
  4. Monitoring Model Performance in Production
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