Are your ML experiments a mess? This common frustration arises when requirements, data sources, and performance metrics shift during development, making it hard to track progress and reproduce results. Effective model governance—tracking experiments, versioning models, and ensuring reproducibility—is essential but often overlooked until models reach production.

ML Flow provides an open‑source MLOps platform that streamlines logging and tracking of models, metrics, and more. After installing ML Flow (`pip install mlflow`) and starting a local server (`mlflow server --host 127.0.0.1 --port 8080`), you create an experiment, start a run, log parameters, metrics such as Adjusted R2, tags, and the model itself using `mlflow.sklearn.log_model`.

Advanced options let you log datasets with `mlflow.data.from_pandas`, automate metric logging via `mlflow.evaluate()`, and record artifacts like visualizations with `mlflow.log_artifact()`. Each run increments the model version, and tags help distinguish development, review, or production stages.

Through the ML Flow UI (http://localhost:8080), you can view experiments, runs, parameters, metrics, and model versions, ensuring that the right models are in production and that results remain reproducible despite changing priorities.