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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Provide an input CSV and a target field to predict, generate a model + code to run it.
| Date | Stars |
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| 2026-07-24 | 1868 |
| 2026-07-25 | 1868 |
| 2026-07-28 | 1868 |
| 2026-07-30 | 1868 |
| 2026-08-06 | 1868 |
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# automl-gs  Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learning model plus native Python code pipelines allowing you to integrate that model into any prediction workflow. No black box: you can see *exactly* how the data is processed, how the model is constructed, and you can make tweaks as necessary.  automl-gs is an AutoML tool which, unlike Microsoft's [NNI](https://github.com/Microsoft/nni), Uber's [Ludwig](https://github.com/uber/ludwig), and [TPOT](https://github.com/EpistasisLab/tpot), offers a *zero code/model definition interface* to getting an optimized model and data transformation pipeline in multiple popular ML/DL frameworks, with minimal Python dependencies (pandas + scikit-learn + your framework of choice). automl-gs is designed for citizen data scientists and engineers without a deep statistical background under the philosophy that you don't need to know any modern data preprocessing and machine learning engineering techniques to create a powerful prediction workflow. Nowadays, the cost of computing many different models and hyperparameters is much lower than the opportunity cost of an data scientist's time. automl-gs is a Python 3 module designed to abstract away the common approaches to transforming tabular data, architecting machine learning/deep learning models, and performing random hyperparameter searches to identify the best-performing model. This allows data scientists and researchers to better utilize their time on model performance optimization. * Generates native Python code; no platform lock-in, and no need to use automl-gs after the model script is created. * Train model configurations super-fast *for free* using a **TPU** and TensorFlow in Google Colaboratory. (in Beta: you can access the Colaboratory notebook [here](https://colab.research.google.com/drive/1sbF8cqnOsdzN9Bdt74eER5s_xXcdvatV)). * Handles messy datasets that normally require manual intervention, such as datetime/categorical encoding and spaced/parenthesized column names. * Each part of the generated model pipeline is its own function w/ docstrings, making it much easier to integrate into production workflows. * Extremely detailed metrics reporting for every trial stored in a tidy CSV, allowing you to identify and visualize model strengths and weaknesses. * Correct serialization of data pipeline encoders on disk (i.e. no pickled Python objects!) * Retrain the generated model on new data without making any code/pipeline changes. * Quit the hyperparameter search at any time, as the results are saved after each trial. * Training progress bars with ETAs for both the overall experiment and per-epoch during the experiment. The models generated by automl-gs are intended to give a very strong *baseline* for solving a given problem; they're not the end-all-be-all that often accompanies the AutoML hype, but the resulting code is easily tweakable to improve from the baseline. You can view the hyperparameters and their values [here](automl_gs/hyperparameters.yml), and the metrics that can be optimized [here](automl_gs/metrics.yml). Some of the more controversial design decisions for the generated models are noted in [DESIGN.md](DESIGN.md). ## Framework Support Currently automl-gs supports the generation of models for regression and classification problems using the following Python frameworks: * TensorFlow (via `tf.keras`) | `tensorflow` * XGBoost (w/ histogram binning) | `xgboost` To be implemented: * Catboost | `catboost` * LightGBM | `lightgbm` ## How to Use automl-gs can be installed [via pip](https://pypi.org/project/automl_gs/): ```shell pip3 install automl_gs ``` You will also need to install the corresponding ML/DL framework (e.g. `tensorflow`/`tensorflow-gpu` for TensorFlow, `xgboost` for xgboost, etc.) After that, you can run it directly from the command li
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d44b234c985b349e, topic:tensorflow