DFFML aims to be the easiest and most convenient way to use Machine Learning.
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It's an AutoML Python library, command line application, and HTTP service.
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You give it your data and tell it what kind of model you want to train. It creates a model for you.
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If you want finer grained control over the model, you can easily do so by implementing your own model plugin.
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We make it easy to use and deploy your models.
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We provide a directed graph concurrent execution environment with managed locking which we call DataFlows.
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DataFlows make it easy to generate datasets or modify existing datasets for rapid iteration on feature engineering.
Documentation for the latest release is hosted at https://intel.github.io/dffml/
Documentation for the master branch is hosted at https://intel.github.io/dffml/master/index.html
The contributing page will guide you through getting setup and contributing to DFFML.
- Ask a question via an issue
- Send an email to dffml-users@lists.01.org
- 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
DFFML is distributed under the MIT License.
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).