Data Flow Facilitator for Machine Learning (dffml)

The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease.

Description

DFFML aims to be the easiest and most convenient way to use Machine Learning.

- Its a machine learning distribution. Providing you access to a set of popular machine learning libraries guaranteed to work together.

- Its a AI/ML Python library, command line application, and HTTP service.

- You give it your data and tell it what kind of model you want to train. It creates a model for you.

- If you want finer grained control over the model, you can easily do so by implementing your own model plugin.

- We make it easy to use and deploy your models.

- We provide a directed graph concurrent execution environment with managed locking which we call DataFlows.

- DataFlows make it easy to generate datasets or modify existing datasets for rapid iteration on feature engineering.

Programming language: Python

License: MIT License

Tags: Natural Language Processing     Command-line Tools     HTTP     RESTful API     Science And Data Analysis     Machine Learning     Python     autoML    

Latest version: v0.4.0

README

Data Flow Facilitator for Machine Learning (dffml)

Mission Statement

DFFML aims to be the easiest and most convenient way to use Machine Learning.

• Its a machine learning distribution. Providing you access to a set of popular machine learning libraries guaranteed to work together.

• Its a AI/ML Python library, command line application, and HTTP service.

• You give it your data and tell it what kind of model you want to train. It creates a model for you.

• If you want finer grained control over the model, you can easily do so by implementing your own model plugin.

• We make it easy to use and deploy your models.

• We provide a directed graph concurrent execution environment with managed locking which we call DataFlows.

• DataFlows make it easy to generate datasets or modify existing datasets for rapid iteration on feature engineering.

Documentation

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

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](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).