MLflow Experiment Tracking
Introduction
MLflow is an open-source platform for managing the ML lifecycle. Its experiment tracking
feature logs parameters, metrics, and artifacts from training runs, making experiments
reproducible and comparable.
Without experiment tracking, data scientists end up with folders named
"model_v2_final_FINAL_actually_final" and no way to know which hyperparameters produced
which results.
Why This Matters
Experiment tracking is the foundation of reproducible ML:
- Every training run is logged with its parameters, metrics, and outputs
- You can compare runs side-by-side
- You can reproduce any previous run
- You have an audit trail of what was tried
From an MLOps perspective, you'll:
- Set up and maintain the MLflow tracking server
- Configure storage backends (S3 for artifacts, database for metadata)
- Integrate MLflow with training pipelines
- Help data scientists access and use the system