MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components:
MLflow Tracking: Record and query experiments: code, data, config, and results
MLflow Projects: Package data science code in a format to reproduce runs on any platform
MLflow Models: Deploy machine learning models in diverse serving environments
Model Registry: Store, annotate, discover, and manage models in a central repository
# Install MLflow
pip install mlflow
# Install MLflow with extra ML libraries and 3rd-party tools
pip install mlflow[extras]
# Install a lightweight version of MLflow
pip install mlflow-skinny
references:
https://mlflow.org/docs/latest/tutorials-and-examples/index.html
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