https://github.com/oskar-j/awesome-auto-ml
Awesome list of AutoML frameworks - curated by @oskar-j
https://github.com/oskar-j/awesome-auto-ml
automl awesome-list feature-engineering hyperparameter-tuning machine-learning model-tuning
Last synced: 3 months ago
JSON representation
Awesome list of AutoML frameworks - curated by @oskar-j
- Host: GitHub
- URL: https://github.com/oskar-j/awesome-auto-ml
- Owner: oskar-j
- License: cc0-1.0
- Created: 2019-07-11T20:22:59.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-02-15T11:06:16.000Z (almost 3 years ago)
- Last Synced: 2024-05-23T07:06:57.310Z (over 1 year ago)
- Topics: automl, awesome-list, feature-engineering, hyperparameter-tuning, machine-learning, model-tuning
- Homepage:
- Size: 25.4 KB
- Stars: 21
- Watchers: 4
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Awesome software for AutoML [](https://github.com/sindresorhus/awesome)
A curated list of awesome AutoML frameworks.
## Frameworks and libraries
### :snake: Python
#### Machine Learning
* [TPOT - Data Science Assistant](https://github.com/EpistasisLab/tpot) - A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. http://epistasislab.github.io/tpot/
* [Featuretools](https://github.com/Featuretools/featuretools) - An open source python framework for automated feature engineering. https://www.featuretools.com
* [Auto-sklearn](https://github.com/automl/auto-sklearn) - Automated Machine Learning with scikit-learn1. https://automl.github.io/auto-sklearn
* [Lightwood](https://github.com/mindsdb/lightwood) - Lightwood is Legos for Machine Learning.
* [MindsDB Native](https://github.com/mindsdb/mindsdb) - Machine Learning in one line of code. http://mindsdb.com
* [mljar-supervised](https://github.com/mljar/mljar-supervised) - Automated Machine Learning for Supervised Tasks: Binary Classification, MultiClass Classification and Regression (work in progress). https://mljar.com
#### Deep learning
* [Auto-Keras](https://github.com/keras-team/autokeras) - Accessible AutoML for deep learning. http://autokeras.com/
* [NNI](https://github.com/microsoft/nni) - An open source AutoML toolkit for neural architecture search and hyper-parameter tuning. https://nni.readthedocs.io/en/latest/
* [AdaNet](https://github.com/tensorflow/adanet) - Fast and flexible AutoML with learning guarantees. https://adanet.readthedocs.io
* [Ludwig](https://github.com/uber/ludwig) - Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. http://ludwig.ai
* [darts](https://github.com/quark0/darts) - Differentiable architecture search for convolutional and recurrent networks. https://arxiv.org/abs/1806.09055
#### Hybrid solutions
* [automl-gs](https://github.com/minimaxir/automl-gs) - Provide an input CSV and a target field to predict, generate a model + code to run it. https://github.com/minimaxir/automl-gs
* [MLBox](https://github.com/AxeldeRomblay/MLBox) - MLBox is a powerful Automated Machine Learning python library. https://mlbox.readthedocs.io/en/latest/
* [mindsdb](https://github.com/mindsdb/mindsdb) - In-Database Machine Learning. MindsDB automates and abstracts machine learning models through virtual AI Tables.
### R
#### Machine Learning
* [forester](https://github.com/ModelOriented/forester) - Full automation of the process of training tree-based models, wrapped into a single `train()` function.
#### Deep learning
* [R interface to Auto-Keras](https://github.com/jcrodriguez1989/autokeras) - Package: R interface to Auto-Keras. http://autokeras.com/
### Scala
#### Machine Learning
* [TransmogrifAI](https://github.com/salesforce/TransmogrifAI) - is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning. https://transmogrif.ai
### Java
#### Machine Learning
* [Glaucus](https://github.com/ccnt-glaucus/glaucus) - A general data-flow-based machine learning suit combining auto machine learning and multiple simplified machine learning algorithm for "unprofessional" data scenitists
## Supplementary tools
### Java
* [Fione: Fess AutoML](https://github.com/codelibs/fione) - AutoML extension for Fess.
### Python
* [PocketFlow](https://github.com/Tencent/PocketFlow) - An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications. https://pocketflow.github.io
* [Ray](https://github.com/ray-project/ray) - A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library. https://ray.readthedocs.io/en/latest/
* [SMAC3](https://github.com/automl/SMAC3) - Sequential Model-based Algorithm Configuration. https://www.automl.org/automated-algorithm-design/algorithm-configuration/smac/
1*Warning: auto-sklearn seems to be unstable at the moment - Travis build is failing and lot of unresolved issues (>100)*