AutoGluon vs EvalML
AutoGluon
auto.gluon.ai
EvalML
evalml.alteryx.comSide by side
| Figure | AutoGluon | EvalML |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · AutoML Software | 8.3 (#6) | 7.8 (#13) |
| Founded | 2019 | 1997 |
| Purpose | AutoGluon automates machine learning for data such as tables and time series, helping users build predictive models with a few lines of code. | EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions. |
| Support | The project welcomes bug reports and feature requests through its GitHub issue tracker. | 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. |
| Time series | Its TimeSeriesPredictor trains multiple models to generate probabilistic forecasts for multiple time series using historical data and related covariates. | EvalML includes time-series functionality for using past values to predict future values, and its documentation says that support is still being actively developed. |
Plans
In their own words
AutoGluon
AutoGluon is a free, open-source Python library for automating machine learning across tabular, time-series, and multimodal data. For tables, it trains classification and regression models while handling data cleaning, feature engineering, hyperparameter optimization, and model selection. Its TimeSeriesPredictor produces probabilistic forecasts for multiple time series using historical data and related covariates. AutoMM reduces manual work in adapting foundation models to specialized data. Documentation examples cover image and text prediction, object detection, image segmentation, document prediction, entity extraction, and semantic matching. AutoMM can adapt models from Hugging Face, TIMM, and MMDetection collections. Forecasting methods include ETS and ARIMA from StatsForecast, LightGBM, DeepAR and Temporal Fusion Transformer from GluonTS, and Chronos. AutoGluon supports Python 3.10 through 3.13 on Linux, macOS, and Windows. GPU usage is not supported on macOS; the installation guide points GPU users to Linux or Windows. AutoGluon is licensed under Apache 2.0. Cloud options include AutoGluon Cloud, described as a free wrapper, and Amazon SageMaker, where compute and S3 storage are billed to the user’s AWS account.