How to use MLflow for experiment tracking

Short answer

MLflow is an open-source platform for managing the machine learning lifecycle, including experiment tracking, model packaging, and deployment.

Steps

1. Start an MLflow tracking server or use the local file-based backend.
2. Begin runs with mlflow.start_run and log parameters, metrics, and artifacts.
3. Organize experiments by name and tag runs for easy filtering.
4. Register promising models in the model registry for staging and production promotion.
5. Retrieve logged artifacts programmatically for comparison and reporting.

Tips

• Log hyperparameters before training and metrics after each epoch.
• Use autologging for frameworks like scikit-learn and PyTorch to reduce boilerplate.
• Store training scripts and conda environments as artifacts for full reproducibility.
• Set up a remote tracking server for team collaboration.

Common issues

• Local file store becoming unwieldy when many experiments are logged.
• Missing artifacts because relative paths changed between runs.
• Database backend configuration errors when scaling to production.
• Overwriting runs when run IDs are not managed carefully.

Example

import mlflow

mlflow.start_run()
mlflow.log_param('epochs', 10)
mlflow.log_metric('accuracy', 0.95)
mlflow.sklearn.log_model(model, 'model')
mlflow.end_run()

This example starts an MLflow run, logs a hyperparameter and a metric, and saves a scikit-learn model artifact for later retrieval.