Enterprise-grade, distributed open-source automated machine learning for high-performance predictive modeling.
H2O AutoML is a core component of the H2O-3 distributed machine learning platform, engineered for high-scale data processing and model optimization. In the 2026 landscape, it remains a premier choice for technical data science teams who require a balance between automation and granular control. The architecture is built on an in-memory, distributed MapReduce framework, allowing it to process massive datasets across a cluster of nodes. H2O AutoML automates the end-to-end machine learning pipeline, including data preprocessing, hyperparameter optimization, and the creation of sophisticated Stacked Ensembles. It supports a wide array of algorithms such as XGBoost, Gradient Boosting Machines (GBM), Generalized Linear Models (GLM), and Deep Learning. Unlike black-box solutions, H2O provides comprehensive model transparency with built-in explainability features like SHAP values and partial dependence plots. Its ability to export models as MOJO (Model Object, Optimized) or POJO (Plain Old Java Object) ensures that transition from experimental R/Python environments to high-frequency production Java/C++ environments is seamless and highly performant.
Automatically constructs two types of ensembles: All Models (all trained base models) and Best of Family (best model from each algorithm type).
Industrial-grade end-to-end MLOps platform for hyper-scale deep learning and GenAI production.
Build, run, and manage AI models at scale with an enterprise-grade collaborative data science platform.
The enterprise-grade studio for foundation models, generative AI, and machine learning.
The engineer's choice for developing, testing, and deploying high-performance AI models.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Data is compressed and distributed across the cluster memory, enabling operations on datasets larger than local RAM.
Exports models into a Model Object, Optimized (MOJO) format which is a standalone Java executable.
Allows users to enforce specific directions (increase/decrease) on feature relationships with the target.
Uses Random Grid Search and early stopping to find optimal parameters within a specified time budget.
Maintains a real-time leaderboard of all models trained during the AutoML run with multiple evaluation metrics.
Integrated suite for generating SHAP, PDP, and Ice plots directly from the model object.
Automating the approval process for loan applications while maintaining regulatory transparency.
Registry Updated:2/7/2026
Deploy via MOJO.
Predicting equipment failure before it occurs to reduce downtime costs.
Optimizing inventory levels across thousands of SKUs.