AutoKeras vs EvalML
AutoKeras
autokeras.com
EvalML
evalml.alteryx.comSide by side
| Figure | AutoKeras | EvalML |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | Yes | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 0 |
| Spec score · AutoML Software | 8.9 (#2) | 7.8 (#13) |
| Installation | The official installation method is pip install autokeras, with Python 3 required. | EvalML can be installed from PyPI, conda-forge, or source, with Python 3.9–3.11 supported on the current installation page. |
| Purpose | AutoKeras is an AutoML system based on Keras, developed to make machine learning accessible to everyone. | EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions. |
| Support | The project directs users to GitHub Discussions to ask questions. | The project directs users to Stack Overflow for usage questions, GitHub issues for bugs and feature requests, Slack for development discussion, and [email protected] for other questions. |
| What it does | AutoKeras is an AutoML library that automates deep-learning model selection and hyperparameter tuning. | EvalML is an AutoML library that builds, optimizes, and evaluates machine-learning pipelines using domain-specific objective functions. |
Plans
In their own words
AutoKeras
AutoKeras is a free, open-source AutoML system based on Keras, designed to make machine learning accessible through code. It supports image and text classification and regression, as well as classification and regression for structured data. Its documentation also includes a tutorial for multi-task or multimodal datasets. Users can assemble custom models from AutoKeras blocks and let the system search for detailed configurations. Image workflows can set architecture type, normalization and data augmentation, or leave those choices for automatic tuning. The best model found can be exported as a Keras Model. The Python package is installed with pip and supports Linux, macOS and Windows, including self-hosted use. The installation guide supports Python 3 and recommends PyTorch version 2.3.0 or later with the PyTorch backend. By default, the final 20% of training data is used for validation, though users can set a different split or supply validation data. Model selection uses validation loss by default, with support for customized metrics and objectives. The project directs users to GitHub Discussions for questions.