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H2O Driverless AI

H2O Driverless AI automates machine learning workflows, including feature engineering, model building, explainability, and deployment.

H2O Driverless AI

In inglese

H2O Driverless AI is an automated machine learning platform for data scientists, IT teams, and business analysts. It imports data, transforms features, trains and validates models, explains predictions, and supports deployment.

It can export scoring pipelines as Python modules or Java artifacts and expose models through REST endpoints or cloud services. The product requires a commercial license; H2O.ai does not publish public tier pricing on the pages reviewed.

Features

  • Automates feature engineering and feature selection
  • Builds, tunes, validates, and selects machine learning models
  • Provides global and local model explanations
  • Includes automatic data visualization and quality checks
  • Supports custom recipes and time-series modeling
  • Exports Python and Java scoring pipelines
  • Deploys models through REST endpoints, cloud services, or edge artifacts
  • Provides a Python client and API

Use cases

  • Build predictive models from tabular business data
  • Detect fraud and other unusual transactions
  • Forecast demand, revenue, or operational metrics
  • Explain model predictions for regulated decisions
  • Deploy scoring pipelines in applications or edge systems
  • Help analysts create models with guided workflows

Pros

    Cons

      Latest updates

      • Version 2.5.0 (August 28, 2026) (2.5.0)

        Added DAICatBoost as a built-in model, batch TTA support, and automatic seasonality and trend detection.

      • Version 2.4.5 (July 29, 2026) (2.4.5)

        Updated the custom H2O-3 build and HMLI to Java 17 / Jetty 12; upgraded MOJO2 runtime and transformers library to 5.x.

      • Version 2.4.4 (June 27, 2026) (2.4.4)

        Migrated pretrained-model download URLs from S3 to CDN and upgraded PyTorch Lightning and AutoDoc.

      • Version 2.4.3 (May 26, 2026) (2.4.3)

        Raise error message in UI when test dataset used by Model Diagnostic does not contain target column.

      • Version 2.4.2 (April 27, 2026) (2.4.2)

        Added cross-workspace AuthZ enforcement for project actions and optimized project listing performance with batch AuthZ filtering.

      Capabilities

      • Charts and dashboards — “present the results in chart format” source
      • Connects to your data — “Ingest data from a variety of data sets including Hadoop HDFS, Amazon S3, and more.” source
      • Builds models — “Quickly create and test highly accurate and robust models with state-of-the-art automated machine learning” source
      • API — “creating a REST endpoint for any web applications to invoke the model” source

      Get it

      Security

      • SOC 2 Type II — “SOC 2 Type 2” source
      • SOC 2 — “SOC 2 Type 2” source
      • ISO 27001 — “ISO/IEC 27001:2022” source
      • GDPR — “Data Processing Agreement” source

      Pricing

      Prices checked
      2026-09-25
      Official website