Recording and comparing different machine learning experiments, including their configurations, metrics, and results. Like keeping a detailed lab notebook to track all your scientific experiments.
MLflow tracks every model training run, recording which parameters were used and how accurate each model was, making it easy to find the best performing version.
All four options provide experiment tracking for ML runs (parameters, metrics, artifacts, and lineage). AWS and GCP offer first-party experiment features inside their managed ML platforms; Azure and OCI commonly use MLflow-compatible tracking integrated into their ML services.