The MLflavors package adds MLflow support for some popular machine learning frameworks currently not considered for inclusion as MLflow built-in flavors. Similar to the built-in flavors, you can use this package to save your model as an MLflow artifact, load your model from MLflow for batch inference, and deploy your model to a serving endpoint using MLflow deployment tools.
The following open-source libraries are currently supported:
Framework Tutorials Category Orbit MLflow-Orbit Time Series Forecasting Sktime MLflow-Sktime Time Series Forecasting StatsForecast MLflow-StatsForecast Time Series Forecasting PyOD MLflow-PyOD Anomaly Detection SDV MLflow-SDV Synthetic Data Generation
The interface design for the supported frameworks is similar to many of the existing built-in flavors. Particularly, the interface for utilizing the custom model loaded as a pyfunc flavor for generating predictions uses a single-row Pandas DataFrame configuration argument to expose the parameters of the flavor's inference API.
readme examples api changelog