Welcome to MLflavors` Documentation

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.