|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Tensorflow Examples||42,312||5 months ago||218||other||Jupyter Notebook|
|TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)|
|Nlp Progress||21,398||19 days ago||45||mit||Python|
|Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.|
|Datasets||15,620||9||208||15 hours ago||52||June 15, 2022||527||apache-2.0||Python|
|🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools|
|Vision||13,585||2,306||1,413||15 hours ago||32||June 28, 2022||891||bsd-3-clause||Python|
|Datasets, Transforms and Models specific to Computer Vision|
|Tensor2tensor||13,223||82||11||4 days ago||79||June 17, 2020||587||apache-2.0||Python|
|Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.|
|Fashion Mnist||9,856||a year ago||24||mit||Python|
|A MNIST-like fashion product database. Benchmark :point_down:|
|Doccano||7,483||22 days ago||28||May 19, 2022||206||mit||Python|
|Open source annotation tool for machine learning practitioners.|
|Facets||7,078||3||1||a month ago||3||July 24, 2019||84||apache-2.0||Jupyter Notebook|
|Visualizations for machine learning datasets|
|Awesome Project Ideas||6,856||15 days ago||1||mit|
|Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas|
|Techniques||6,096||a day ago||1||apache-2.0|
|Techniques for deep learning with satellite & aerial imagery|
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. DFFMLs 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 dragn 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, whod 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 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
The contributing page will guide you through getting setup and contributing to DFFML.
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).