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Mission Statement

As we all know the Machine Learning space has a lot of tools and libraries for creating pipelines to train, test & deploy models, and dealing with these many different APIs can be cumbersome.

Our project aims to make this process a breeze by introducing interoperability under a modular and easily extensible API. DFFML’s plugin-based architecture makes it a swiss army knife of ML research & MLOps.

We heavily rely on DataFlows, which are basically directed graphs. We are also working on a WebUI to make dataflows completely a drag’n drop experience. Currently, all of our functionalities are accessible through Python API, CLI, and HTTP APIs.

We broadly have two types of audience here, one is Citizen Data Scientists and ML researchers, who’d probably use the WebUI to experiment and design models. MLOps people will deploy models and set up data processing pipelines via the HTTP/CLI/Python APIs.

Documentation

Documentation for the latest release is hosted at https://intel.github.io/dffml/

Documentation for the main branch is hosted at https://intel.github.io/dffml/main/index.html

Contributing

The contributing page will guide you through getting setup and contributing to DFFML.

Help

• Ask a question via an issue
• Send an email to [email protected]
  • You can subscribe to the users mailing list here https://lists.01.org/postorius/lists/dffml-users.lists.01.org/
• Ask a question on the Gitter chat

License

DFFML is distributed under the MIT License.

Legal

This software is subject to the U.S. Export Administration Regulations and other U.S. law, and may not be exported or re-exported to certain countries (Cuba, Iran, Crimea Region of Ukraine, North Korea, Sudan, and Syria) or to persons or entities prohibited from receiving U.S. exports (including Denied Parties, Specially Designated Nationals, and entities on the Bureau of Export Administration Entity List or involved with missile technology or nuclear, chemical or biological weapons).