State-of-the-art AutoML for tabular, image, text, and time-series data using multi-layer stacking.
AutoGluon is an advanced open-source AutoML framework developed by AWS Labs, engineered to deliver high-performance machine learning models with minimal human intervention. By 2026, it has solidified its position as the industry standard for 'multi-layer stacking,' a technique that prioritizes model ensembling over traditional, compute-expensive hyperparameter optimization. The framework's architecture is uniquely modular, allowing it to fuse disparate data types—such as tabular records, raw text, and images—into a single predictive pipeline. It automates critical tasks including data cleaning, feature engineering, and neural architecture search, consistently winning Kaggle-level competitions with out-of-the-box settings. AutoGluon is particularly valued in enterprise environments for its 'presets' system, which allows developers to trade off between training time and predictive accuracy (e.g., 'best_quality' vs. 'medium_quality'). Its integration with Ray enables massive distributed training across clusters, making it scalable from local workstations to global cloud infrastructures. As of 2026, it remains the go-to solution for teams requiring production-grade models without the overhead of manual model selection and tuning.
AutoGluon is an advanced open-source AutoML framework developed by AWS Labs, engineered to deliver high-performance machine learning models with minimal human intervention.
Explore all tools that specialize in classify images. This domain focus ensures AutoGluon delivers optimized results for this specific requirement.
Explore all tools that specialize in multi-layer stacking. This domain focus ensures AutoGluon delivers optimized results for this specific requirement.
AutoGluon uses a multi-layer stacking strategy where models in the first layer provide predictions as features to the next layer.
Directly trains on datasets containing text, images, and tabular data simultaneously using Transformer-based backbones.
Uses Bayesian Optimization and Hyperband to efficiently search for optimal parameters when ensembling is not sufficient.
Automatically detects data types, handles missing values, and performs one-hot encoding or embedding generation.
Pre-trained zero-shot time-series forecasting models based on language model architectures.
Compresses large ensembles into a single, smaller 'proxy' model (e.g., a single Random Forest or LightGBM).
Natively integrates with the Ray framework to distribute 'fit' calls across multi-node clusters.
Provision a Python 3.9+ environment with at least 16GB RAM.
Install AutoGluon using 'pip install autogluon' ensuring CUDA drivers are present for GPU acceleration.
Import the relevant predictor module (e.g., TabularPredictor) into your script.
Load your dataset into an AutoGluon TabularDataset object.
Define the target column name and the evaluation metric (e.g., roc_auc or accuracy).
Initialize the Predictor and call .fit() with the desired 'presets' configuration.
Monitor training progress through the logged leaderboard, which displays model ranks.
Evaluate the best-performing model on a separate test set using .evaluate().
Perform batch inference using .predict() or .predict_proba().
Serialize the model artifact for deployment via .save() or export to ONNX format.
All Set
Ready to go
Verified feedback from other users.
"Users praise its 'insane' accuracy relative to the effort required. It is widely considered the best AutoML tool for tabular data."
Post questions, share tips, and help other users.
Autonomous Data Intelligence for Real-Time Predictive Insights and Neural Analytics.
The Unified Platform for Predictive and Generative AI Governance and Delivery.
Automated Multimodal Image Recognition and SEO-Optimized Alt-Text Generation
Hierarchical Vision Transformer using Shifted Windows for general-purpose computer vision tasks.
A pure ConvNet model constructed entirely from standard ConvNet modules, designed for the 2020s.
The high-performance deep learning framework for flexible and efficient distributed training.
