About H2O Driverless AI
H2O Driverless AI auto-generates feature engineering and tuned models. Strong for tabular data and time-series forecasting. H2O Driverless AI stands out for automatic feature engineering, model explainability, multi-model llm support, and no-code llm fine-tuning. Teams pick it because automatic feature engineering and strong on tabular data. Things to keep in mind: driverless ai license is pricey.
H2O Driverless AI is a paid tool from H2O.ai and launched on 2017-01-30. It sits in the Research and Machine Learning space and is best used to Fraud detection and prevention in financial services, Call center operations optimization, KYC and customer onboarding automation.
Pricing
Free Trial$0
- Full Driverless AI functionality during trial period
- Access to automated machine learning capabilities
- Single user license
H2O.ai CloudUsage-based
- Cloud-hosted Driverless AI environment
- Hourly compute billing
- Scalable infrastructure
Commercial LicenseCustom
- On-premise or cloud deployment
- Multi-user enterprise license
- Annual or multi-year contracts starting ~$70k/year
Features
- •Automatic feature engineeringBuilt-in support for automatic feature engineering — used for fraud detection and prevention in financial services.
- •Model explainabilityBuilt-in support for model explainability — used for fraud detection and prevention in financial services.
- •Multi-model LLM supportBuilt-in support for multi-model llm support — used for fraud detection and prevention in financial services.
- •No-code LLM fine-tuningBuilt-in support for no-code llm fine-tuning — used for fraud detection and prevention in financial services.
- •Autonomous agents with human-in-the-loopBuilt-in support for autonomous agents with human-in-the-loop — used for fraud detection and prevention in financial services.
- •Air-gapped and on-premises deploymentBuilt-in support for air-gapped and on-premises deployment — used for fraud detection and prevention in financial services.
- •Tabular data foundation modelBuilt-in support for tabular data foundation model — used for fraud detection and prevention in financial services.
- •MLOps and model monitoringBuilt-in support for mlops and model monitoring — used for fraud detection and prevention in financial services.
Pros
- Automatic feature engineering
- Strong on tabular data
- Open-source H2O-3 available free
Cons
- Driverless AI license is pricey
- Steeper learning curve than DataRobot
- Visualizations dated
AI Models used
The foundation models that power H2O Driverless AI under the hood.
Powers automated feature engineering, model selection, and hyperparameter tuning for machine learning workflows.
Enables natural language interfaces and automated explanation generation for model insights and recommendations.
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