This page contains a moderated list of examples, tutorials, articles, and research papers about AutoGluon use cases. It is inspired by awesome-machine-learning.
We will be happy to add your success story using AutoGluon to this list. Send us a pull request if you want to include your case here.
To get started, we recommend watching AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code, our talk at AutoML Conf 2023.
- AutoGluon-TimeSeries: Every Time Series Forecasting Model In One Library (Towards Data Science, Jan 2024)
- AutoGluon-TimeSeries: Creating Powerful Ensemble Forecasts - Complete Tutorial (AI Horizon Forecast, Dec 2023)
- 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)
AutoGluon is widely adopted on ML competition sites such as Kaggle. Below is a sampling of competition solutions that use AutoGluon to achieve strong results.
| Placement | Competition Solution | Author | Date | AutoGluon Details | Notes |
|---|---|---|---|---|---|
| 🥇 Rank 1/4370 | Predicting Heart Disease | Masaya Kawamata | 2026/03/01 | v1.5, Tabular | Kaggle Playground Series S6E2. Also used in the 16th place solution! |
| 🥉 Rank 3/4317 | Predicting Student Test Scores | Funguscakehead | 2026/01/31 | v1.5, Tabular | Kaggle Playground Series S6E1. Also used in the 6th and 14th place solutions! |
AutoGluon continued to see heavy usage in top Kaggle competition solutions in 2025, most notably with 🥇 1st and 🥈 2nd place solutions in two high profile $50,000 prize money competitions.
Quote from Kaggle Grandmaster James Day, the 5th highest rated Kaggler in the world, on his 🥇 winning AutoGluon solution to Kaggle's $50,000 prize money NeurIPS Open Polymer Prediction 2025 Competition:
My solution is an ensemble of BERT, AutoGluon, and Uni-Mol models.
AutoGluon's "best" quality preset with a 2 hour limit for each property was able to beat an ensemble of XGBoost, LightGBM, and TabM models that I tuned with Optuna and ~20x that amount of compute (not counting data preprocessing tuning, which was in the ballpark of ~1 day per downstream prediction library I paired it with, or all the other models I tried before settling on XGB + LGBM + TabM for the relatively manual ensemble).
Its wMAE score was ~2% better than the relatively manual ensemble, good enough that it was not useful to make an ensemble of AutoGluon + my more manually constructed ensemble.
This was my first time using AutoGluon, and I found it very impressive. I was absolutely gob-smacked by AutoGluon's efficiency.
Broadly speaking, I think the main benefits of my manual involvement were located in the data preparation, post-processing, and non-tabular model selection/tuning aspects of the competition. AutoGluon was embarrassingly hard to beat on the tabular modeling side of things.
Quote from 7x Kaggle Grandmaster Chris Deotte, the 4th highest rated Kaggler in the world, on his 🥇 winning solution to the Predict Podcast Listening Time competition:
My first single model with lots of feature engineering was beat by AutoGluon. My model had CV/LB 12.5 and AutoGluon had CV/LB 12.4. This was weird because AutoML has never beat my single models before. (AutoML doesn't feature engineer nor target encode, so it was very surprising to see such good performance here).
The official $50,000 2025 Meta Kaggle Hackathon 🥈 2nd place Trends Over Time Writeup highlighted AutoGluon alongside OpenAI, HuggingFace, and Transformers as prominent emerging technologies within the Kaggle ecosystem:
First, we examine the evolution of imported packages in competition kernels: xgboost dominated early years, later replaced by lightgbm, tensorflow, and transformers.
Recent years (2022–2025) show emerging use of autogluon, optuna, and openai, reflecting interest in AutoML and generative models.
Automation and deployment tools like autogluon, huggingface, and sagemaker reflect a shift toward streamlined workflows.
Over time, we observe increasing entropy and diversity in both package imports and method calls, particularly in competition settings where adaptation to new tools is quick. Early dominance by xgboost has shifted toward modern libraries like lightgbm, transformers, and autogluon.
| Placement | Competition Solution | Author | Date | AutoGluon Details | Notes |
|---|---|---|---|---|---|
| 🥈 Rank 2/3850 | Predicting Loan Payback | AngelosMar | 2025/11/30 | v1.4, Tabular | Kaggle Playground Series S5E11. Also used in the 8th place solution! |
| 🥇 Rank 1/26 | Hill of Towie Wind Turbine Power Prediction | Conor Malone | 2025/11/19 | v1.4, Tabular | Community Competition hosted by Gabe. Also used in the 4th place solution! |
| Rank 8/4082 (Top 0.2%) | Predicting Road Accident Risk | Matt Graham | 2025/11/01 | v1.4, Tabular | Kaggle Playground Series S5E10. AutoGluon was also used in prototyping for the 1st place solution. |
| 🥇 Rank 1/172 | Dig4Bio Raman Transfer Learning Challenge | Paritosh Kumar Tripathi | 2025/09/26 | v1.4, Tabular | $1500 prize competition. |
| 🥇 Rank 1/2240 | NeurIPS - Open Polymer Prediction 2025 | James Day | 2025/09/15 | v1.4, Tabular | $50,000 prize competition. Also used in the 24th place solution! |
| 🥇 Rank 1/3365 | Binary Classification with a Bank Dataset | Optimistix | 2025/08/31 | v1.4, Tabular | Kaggle Playground Series S5E8. Also used in the 🥉 3rd, 4th, 5th, 8th, 10th, 11th, and 17th place solutions! |
| 🥈 Rank 2/691 | Prediction Interval Competition II: House price | Masaya Kawamata | 2025/07/27 | v1.4, Tabular | Community Competition hosted by Kaggle Grandmaster Carl McBride Ellis. |
| 🥉 Rank 3/2648 | Predicting Optimal Fertilizers | Mahog | 2025/07/01 | v1.4, Tabular | Kaggle Playground Series S5E6. Also used in 4th, 10th, 23rd and 28th place solutions! |
| 🥈 Rank 2/4316 | Predict Calorie Expenditure | Mahog | 2025/06/01 | v1.3, Tabular | Kaggle Playground Series S5E5. Also used in the 🥉 3rd, 4th, 6th, 7th, and 13th place solutions! |
| 🥉 Rank 3/694 | Russian Car Plates Prices Prediction | bestwater | 2025/05/30 | v1.3, Tabular | $50 prize competition. |
| 🥇 Rank 1/3310 | Predict Podcast Listening Time | Chris Deotte | 2025/05/01 | v1.3, Tabular | Kaggle Playground Series S5E4. Also used in the 4th and 5th place solutions! |
| 🥈 Rank 2/3325 | CIBMTR - Equity in post-HCT Survival Predictions | Anil Ozturk & team | 2025/03/06 | v1.3, Tabular | $50,000 prize competition. Also used in the 5th, 12th, and 24th place solutions! |
| Rank 5/3393 (Top 0.2%) | Backpack Prediction Challenge | Optimistix | 2025/02/28 | v1.3, Tabular | Kaggle Playground Series S5E2. |
| Placement | Competition Solution | Author | Date | AutoGluon Details | Notes |
|---|---|---|---|---|---|
| 🥈 Rank 2/2392 (Top 0.1%) | Regression with an Insurance Dataset | SCRIPTCHEF | 2024/12/31 | v1.2, Tabular | Kaggle Playground Series S4E12. Also used in 9th and 10th place solutions! |
| 🥇 Rank 1/2687 | Exploring Mental Health Data | Mahdi Ravaghi | 2024/11/30 | v1.1, Tabular | Kaggle Playground Series S4E11. Also used in 4th and 13th place solutions! |
| Rank 8/3859 (Top 0.3%) | Loan Approval Prediction | Mahdi Ravaghi | 2024/10/31 | v1.1, Tabular | Kaggle Playground Series S4E10 |
| 🥇 Rank 1/3066 | Regression of Used Car Prices | Mart Preusse | 2024/09/30 | v1.1, Tabular | Kaggle Playground Series S4E9. Also used in 🥈 2nd, 🥉 3rd, 4th, and 5th place solutions! |
| 🥇 Rank 1/1116 | Kaggle AutoML Grand Prix (Overall) | Alexander R., Dmitry S., Rinchin | 2024/09/01 | v1.1, Tabular | Teams using AutoGluon in the Grand Prix: 🥇 1st, 🥈 2nd, 🥉 3rd, 4th, 6th, 7th, 8th, 9th, and 10th place teams! |
| 🥈 Rank 2/247 (Top 1%) | Kaggle AutoML Grand Prix Episode 5 | Robert Hatch | 2024/09/01 | v1.1, Tabular | Also used in 🥉 3rd, 4th, 6th, 7th, 9th, and 10th place solutions! |
| 🥇 Rank 1/2424 | Binary Prediction of Poisonous Mushrooms | Optimistix | 2024/08/31 | v1.1, Tabular | Kaggle Playground Series S4E8. Also used in 🥈 2nd, 🥉 3rd, 4th, 6th, 8th, and 10th place solutions! |
| 🥇 Rank 1/218 | Kaggle AutoML Grand Prix Episode 4 | Lennart P., Nick E. & Arjun K. | 2024/08/01 | v1.1, Tabular | Also used in 🥈 2nd, 🥉 3rd, 4th, 5th, 6th, 7th, 8th, 9th, and 10th place solutions! |
| 🥉 Rank 3/2236 (Top 0.2%) | Binary Classification of Insurance Cross Selling | Tilii | 2024/07/31 | v1.1, Tabular | Kaggle Playground Series S4E7 |
| Rank 4/207 (Top 2%) | Kaggle AutoML Grand Prix Episode 3 | Lennart Purucker & Nick Erickson | 2024/07/01 | v1.1, Tabular | |
| Rank 17/2684 (Top 1%) | Classification with an Academic Success Dataset | Mart Preusse | 2024/06/30 | v1.1, Tabular | Kaggle Playground Series S4E6 |
| 🥉 Rank 3/542 (Top 0.6%) | WiDS Datathon 2024 Challenge #2 | olgaskv | 2024/06/11 | v1.1, Tabular | |
| 🥇 Rank 1/230 | Kaggle AutoML Grand Prix Episode 2 | Lennart Purucker & Nick Erickson | 2024/06/01 | v1.1, Tabular | Also used in 5th place solution! |
| 🥇 Rank 1/2788 | Regression with a Flood Prediction Dataset | Alexandre Daubas | 2024/05/31 | v1.1, Tabular | Kaggle Playground Series S4E5. Also used in 🥈 2nd, 🥉 3rd, and 4th place solutions! |
| Rank 5/214 (Top 3%) | Kaggle AutoML Grand Prix Episode 1 | James King | 2024/05/01 | v1.1, Tabular | Also used in 8th and 9th place solutions! |
| 🥇 Rank 1/2606 | Regression with an Abalone Dataset | Johannes Heller | 2024/04/30 | v1.0, Tabular | Kaggle Playground Series S4E4. Also used in 🥈 2nd, 🥉 3rd, 4th, and 8th place solutions! |
| 🥉 Rank 3/2303 (Top 0.2%) | Steel Plate Defect Prediction | Samvel Kocharyan | 2024/03/31 | v1.0, Tabular | Kaggle Playground Series S4E3 |
| 🥈 Rank 2/93 (Top 2%) | Prediction Interval Competition I: Birth Weight | Oleksandr Shchur | 2024/03/21 | v1.0, Tabular | |
| 🥈 Rank 2/1542 (Top 0.2%) | WiDS Datathon 2024 Challenge #1 | lazy_panda | 2024/03/01 | v1.0, Tabular | |
| 🥈 Rank 2/3746 (Top 0.1%) | Multi-Class Prediction of Obesity Risk | Kirderf | 2024/02/29 | v1.0, Tabular | Kaggle Playground Series S4E2 |
| 🥈 Rank 2/3777 (Top 0.1%) | Binary Classification with a Bank Churn Dataset | lukaszl | 2024/01/31 | v1.0, Tabular | Kaggle Playground Series S4E1 |
| Placement | Competition Solution | Author | Date | AutoGluon Details | Notes |
|---|---|---|---|---|---|
| Rank 4/1718 (Top 0.2%) | Multi-Class Prediction of Cirrhosis Outcomes | Kirderf | 2023/12/31 | v1.0, Tabular | Kaggle Playground Series S3E26 |
| 🥈 Rank 2/58 (Top 4%) | ML Olympiad - Water Quality Prediction | Chris X | 2023/03/11 | v0.6.2, Tabular | |
| Rank 6/734 (Top 1%) | Tabular Regression with a Gemstone Price Dataset | Kirderf | 2023/03/06 | v0.6.2, Tabular | Kaggle Playground Series S3E8 |
| Rank 9/703 (Top 1.3%) | Tabular Regression with a Paris Housing Price Dataset | Brendan Moore | 2023/02/20 | v0.6.2, Tabular | Kaggle Playground Series S3E6 |
| 🥇 Rank 1/689 | Tabular Regression with the California Housing Dataset | Kirderf | 2023/01/09 | v0.6.1, Tabular | Kaggle Playground Series S3E1 |
To view a list of all AutoGluon research papers, please refer to our citation guide.
AMLB: An AutoML Benchmark (JMLR 2024)
- For a thorough comparison of AutoGluon and other modern AutoML systems, please refer to the 2024 JMLR paper "AMLB: An AutoML Benchmark" and the 2022 edition where AutoGluon is shown to be the state-of-the-art among AutoML systems on tabular data.
- We encourage all users to benchmark AutoGluon & other AutoML frameworks on AMLB.
- This is our preferred benchmark as it is widely accepted and trusted within the AutoML community.
The AutoML Benchmark 2025, an independent large-scale evaluation of tabular AutoML frameworks, showcases AutoGluon 1.2 as the state of the art AutoML framework. Highlights include:
- AutoGluon's rank statistically significantly outperforms all AutoML systems via the Nemenyi post-hoc test across all time constraints.
- AutoGluon with a 5 minute training budget outperforms all other AutoML systems with a 1 hour training budget.
- AutoGluon is pareto efficient in quality and speed across all evaluated presets and time constraints.
- AutoGluon with
presets="high", infer_limit=0.0001(HQIL in the figures) achieves >10,000 samples/second inference throughput while outperforming all methods. - AutoGluon is the most stable AutoML system. For "best" and "high" presets, AutoGluon has 0 failures on all time budgets >5 minutes.
Below is a sampling of some interesting papers that have cited AutoGluon.
- (2023/04/28) Benchmarking Automated Machine Learning Methods for Price Forecasting Applications
- This paper compares various traditional and AutoML methods for price forecasting problems, with AutoGluon achieving the strongest results.