Autogluon
Fast and Accurate ML in 3 Lines of Code
Install / Use
/learn @autogluon/AutogluonREADME
Fast and Accurate ML in 3 Lines of Code
Installation | Documentation | Release Notes
</div>AutoGluon, developed by AWS AI, automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, time series, and tabular data.
💾 Installation
AutoGluon is supported on Python 3.10 - 3.13 and is available on Linux, MacOS, and Windows.
You can install AutoGluon with:
pip install autogluon
Visit our Installation Guide for detailed instructions, including GPU support, Conda installs, and optional dependencies.
:zap: Quickstart
Build accurate end-to-end ML models in just 3 lines of code!
from autogluon.tabular import TabularPredictor
predictor = TabularPredictor(label="class").fit("train.csv", presets="best")
predictions = predictor.predict("test.csv")
| AutoGluon Task | Quickstart | API |
|:--------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| TabularPredictor | 
|
| TimeSeriesPredictor | |
| MultiModalPredictor | |
:mag: Resources
Hands-on Tutorials / Talks
Below is a curated list of recent tutorials and talks on AutoGluon. A comprehensive list is available here.
| Title | Format | Location | Date | |--------------------------------------------------------------------------------------------------------------------------|----------|----------------------------------------------------------------------------------|------------| | :tv: Structured Foundation Models Meets AutoML | Expo Talk | ICML 2025 | 2025/07/13 | | :tv: AutoGluon 1.2: Advancing AutoML with Foundational Models and LLM Agents | Expo Workshop | NeurIPS 2024 | 2024/12/10 | | :tv: AutoGluon: Towards No-Code Automated Machine Learning | Tutorial | AutoML 2024 | 2024/09/09 | | :tv: AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code | Tutorial | AutoML 2023 | 2023/09/12 | | :sound: AutoGluon: The Story | Podcast | The AutoML Podcast | 2023/09/05 | | :tv: AutoGluon: AutoML for Tabular, Multimodal, and Time Series Data | Tutorial | PyData Berlin | 2023/06/20 | | :tv: Solving Complex ML Problems in a few Lines of Code with AutoGluon | Tutorial | PyData Seattle | 2023/06/20 | | :tv: The AutoML Revolution | Tutorial | Fall AutoML School 2022 | 2022/10/18 |
Scientific Publications
- AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (Arxiv, 2020) (BibTeX)
- Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation (NeurIPS, 2020) (BibTeX)
- Benchmarking Multimodal AutoML for Tabular Data with Text Fields (NeurIPS, 2021) (BibTeX)
- XTab: Cross-table Pretraining for Tabular Transformers (ICML, 2023)
- AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting (AutoML Conf, 2023) (BibTeX)
- TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications (AutoML Conf, 2024)
- AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models (AutoML Conf, 2024) (BibTeX)
- Chronos: Learning the Language of Time Series (TMLR, 2024)
- Multi-layer Stack Ensembles for Time Series Forecasting (AutoML Conf, 2025) (BibTeX)
- Chronos-2: From Univariate to Universal Forecasting (Arxiv, 2025) (BibTeX)
- TabArena: A Living Benchmark for Machine Learning on Tabular Data (NeurIPS Spotlight, 2025)
- Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models (NeurIPS, 2025)
- MLZero: A Multi-Agent System for End-to-end Machine Learning Automation (NeurIPS, 2025)
- fev-bench: A Realistic Benchmark for Time Series Forecasting (Arxiv, 2025)
Articles
- AutoGluon-TimeSeries: Every Time Series Forecasting Model In One Library (Towards Data Science, Jan 2024)
- AutoGluon for tabular data: 3 lines of code to achieve top 1% in Kaggle competitions (AWS Open Source Blog, Mar 2020)
- AutoGluon overview & example applications (Towards Data Science, Dec 2019
