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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, and how the model is constructed, and you can make tweaks as necessary. automl-gs is an AutoML tool which, unlike Microsoft's NNI, Uber's Ludwig, and 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.
Features
- 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
- 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
- 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
Project Samples
