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 help log and compare ML runs (parameters, metrics, artifacts) to reproduce results and choose the best model. SageMaker Experiments and Vertex AI Experiments provide native experiment/run tracking in their platforms, while Azure ML and OCI Data Science commonly use MLflow Tracking integrated into their managed ML environments.