| microsoft/nni |
13,536 |
|
8 |
27 |
over 2 years ago |
55 |
September 14, 2023 |
342 |
mit |
Python |
| An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning. |
| EpistasisLab/tpot |
9,385 |
|
40 |
22 |
over 2 years ago |
62 |
August 15, 2023 |
284 |
lgpl-3.0 |
Python |
| A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. |
| alteryx/featuretools |
6,940 |
|
35 |
43 |
about 2 years ago |
103 |
October 26, 2023 |
176 |
bsd-3-clause |
Python |
| An open source python library for automated feature engineering |
| mljar/mljar-supervised |
2,867 |
|
0 |
2 |
about 2 years ago |
84 |
September 26, 2023 |
141 |
mit |
Python |
| Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation |
| salesforce/TransmogrifAI |
2,099 |
|
0 |
3 |
about 4 years ago |
9 |
June 11, 2020 |
44 |
bsd-3-clause |
Scala |
| TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning |
| ClimbsRocks/auto_ml |
1,442 |
|
1 |
6 |
almost 7 years ago |
78 |
February 22, 2018 |
182 |
mit |
Python |
| [UNMAINTAINED] Automated machine learning for analytics & production |
| DeepWisdom/AutoDL |
999 |
|
0 |
0 |
over 3 years ago |
2 |
May 18, 2020 |
23 |
apache-2.0 |
Python |
| Automated Deep Learning without ANY human intervention. 1'st Solution for AutoDL challenge@NeurIPS. |
| sberbank-ai-lab/LightAutoML |
769 |
|
0 |
0 |
about 4 years ago |
2 |
November 03, 2023 |
6 |
apache-2.0 |
Python |
| LAMA - automatic model creation framework |
| SimonBlanke/Hyperactive |
475 |
|
0 |
5 |
over 2 years ago |
75 |
October 24, 2023 |
8 |
mit |
Python |
| An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models. |
| Western-OC2-Lab/AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics |
443 |
|
0 |
0 |
almost 3 years ago |
0 |
|
0 |
mit |
Jupyter Notebook |
| Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch and online/continual learning |
