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. Just like 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
Time Series Forecasting
Time Series Forecasting
Time Series Forecasting
Anomaly Detection
Synthetic Data Generation
The MLflow interface for the supported frameworks closely follows the design of 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.