Predictive AI that reaches production, and stays there.
Forecast demand, score risk, detect fraud, and explain every prediction to a regulator. Built on the AutoML engine that has run inside banks and telcos for a decade, and now driven by the H2O AI Super Agent.
The H2O Predictive AI Platform covers AutoML, explainability, deep learning, a tabular foundation model, a feature store, and MLOps on one platform.
H2O Driverless AI has shipped AutoDoc (automatic model documentation) and AutoMLI (reason codes, K-LIME, Shapley) since 2017.
tabH2O is a tabular foundation model that returns predictions without training, under three seconds on 15,000 rows.
Commonwealth Bank of Australia scores 16 million customers in real time on H2O.ai and cut scam losses by 76%.
The Data Science Agent in the H2O AI Super Agent drives these engines from a plain-English request.
What is Predictive AI
Predictive AI turns historical data into forecasts, risk scores, and fraud signals a business can act on.
Building the model is the easy part. Predictive programs break in four predictable places, from the data that goes in to the drift that sets in.
01UntraceableFeatures get rebuilt for every project, training and serving drift apart, and nobody can say which data trained the model that's in production.
02UnreproducibleToo many approaches run for too long, and the winning model can't be repeated. The deep-learning work needs a research team you don't have.
03UnexplainableThe model works and no one can say why. Risk review drags for weeks, then sends it back for documentation that was never written.
04UndetectedScoring quietly misses its SLA and accuracy drifts with no alert. There is no rollback, so the only fix is to start a whole new project.
The record
2017AutoDoc and AutoMLI shipped
20,000+organizations on H2O open source
16M+customers scored in real time at CBA
Visionary2026 Gartner MQ, AI Platforms
What the H2O Predictive AI Platform does
Every stage automated. Every stage explainable.
H2O AI built a component for each of those failures, then connected them into one platform. A model goes from raw data to a governed endpoint in production without ever leaving it.
Automated feature engineeringFeature engineering by evolutionary search, model selection, hyperparameter tuning, and ensembling, on GPU. Bring your own recipes as first-class citizens.
Explainability built inAutoDoc writes the model documentation. AutoMLI gives K-LIME, Shapley, surrogate trees, disparate impact, and a reason code for every prediction, shipping since 2017.
Predictions without trainingSend labeled and unlabeled rows, get predictions back. No training run, no tuning, no artifact to deploy, in under three seconds on 15,000 rows.
The Data Science Agent is one of nine agent types in the H2O AI Super Agent. It drives Driverless AI, runs H2O-3 in Notebook Labs, calls tabH2O for instant predictions, and hands the result to MLOps. It does not replace the engines. It operates them, with the same AutoDoc and reason codes attached to everything it produces.
Plain English in"Build me a churn model on last quarter's data and document it." The agent plans the experiment, runs it, and returns the AutoDoc.
Orchestrate, don't rebuildYour existing DAI projects, H2O-3 pipelines, and feature definitions stay where they are. The agent reaches into them.
Where it fits
One lane from data to decision. Observability underneath all of it.
The Feature Store feeds the training engines and, separately, feeds tabH2O for instant predictions. MLOps serves models to apps and APIs. The Super Agent drives the engines. Highlighted boxes are the H2O Predictive AI Platform.
Run it anywhere
You operate it, or H2O AI operates it for you.
Same platform either way: the same capabilities, the same governance, and the same audit trail. The choice stays open, so you can switch between them as the business changes, without rebuilding what runs on top.
Each turn made the lifecycle simpler for the people who run it.
AutoML automated the science. AutoAI automated the outcome. Agentic AI automates the thinking. Each era absorbed the one before it, and H2O AI kept building through all three.
2017AutoML ships.H2O-3 AutoML and Driverless AI ship with AutoDoc and AutoMLI. Explainability from day one.
2022No-code deep learning.Hydrogen Torch arrives; Feature Store co-developed with AT&T.
2024One platform.Notebook Labs and AI Engine Manager land in H2O AI Cloud (now the H2O Sovereign AI Platform).
No-code deep learningImage, text, video, and audio models from Kaggle Grandmaster defaults. Transfer learning, multi-GPU, and one-click packaging into MLOps, no research team needed.
Features, defined onceOne catalog with 40+ attributes per feature. Offline for training, online for sub-millisecond serving, with drift alerts that can trigger retraining.
Deploy, govern, monitorDeploy any model with one-click rollback. Drift, accuracy, and fairness monitors retrain on threshold, with an audit log for every scoring request.
The engine behind H2O.ai's largest production deployments.
Governed by defaultEvery action is logged, every model is explainable, every deployment goes through MLOps. Human in the loop where you want it.
Deliberate boundaries: the Feature Store does not feed H2O Cascade, and MLOps does not deploy to h2oGPTe. Generative and predictive lanes meet in the Super Agent, not in the plumbing.
The H2O AI Cloud for Government has achieved FedRAMP High Certification, delivering the highest standard of cloud security so federal agencies can harness predictive and generative AI without compromising data sovereignty or compliance.
TelecommunicationsAT&T~45 billion tokens on an average day, and a fine-tuned SLM at 91% accuracy for 35% of the prior cost. The Feature Store was co-developed with AT&T's own data teams.Read the case study
2026tabH2O.A tabular foundation model, predictions with no training run at all.
2026The Data Science Agent.Drives the whole lane from a sentence. Gartner Visionary, fourth year running.
The H2O Generative AI Platform: Agentic RAG with citations, Document AI, any-model routing, and a secure model factory to fine-tune small models you own.
H2O Driverless AI (DAI) is an award-winning AutoML platform that automates feature engineering, model selection, hyperparameter tuning, and ensembling, and generates AutoDoc model documentation and AutoMLI interpretability (K-LIME, Shapley, surrogate trees, disparate impact, reason codes) with every model. It has shipped since 2017.
