Overview

H2O Driverless AI is an artificial intelligence (AI) platform for automatic machine learning. Driverless AI automates some of the most difficult data science and machine learning workflows, such as feature engineering, model validation, model tuning, model selection, and model deployment. It aims to achieve the highest predictive accuracy, comparable to expert data scientists, but in a much shorter time thanks to end-to-end automation. Driverless AI also offers automatic visualization and machine learning interpretability (MLI). Especially in regulated industries, model transparency and explanation are just as important as predictive performance. Modeling pipelines (feature engineering and models) are exported (in full fidelity, without approximations) both as Python modules and as Java standalone scoring artifacts.

Apart from the standard for model building, DAI offers an that makes it simple for you to set up a Driverless AI experiment and ensure that the experiment’s settings are optimally configured for your specific use case.

Driverless AI runs on commodity hardware. It was also specifically designed to take advantage of graphics processing units (GPUs), including multi-GPU workstations and servers such as the NVIDIA DGX-1 for orders of magnitude faster training.

This document describes how to install and use Driverless AI. For more information about Driverless AI, see https://www.h2o.ai/products/h2o-driverless-ai/.

For a third-party review, see .

Have Questions?

If you have questions about using Driverless AI, post them on Stack Overflow using the driverless-ai tag at http://stackoverflow.com/questions/tagged/driverless-ai.

You can also post questions on the H2Oai.cc Community Slack workspace in the #driverlessai channel. If you have not signed up for the H2Oai.cc Community Slack workspace, you can do so here: https://www.h2o.ai/community/.

Release Notes

Introduction

Licensing and Version Support

Installation and Upgrade

Configuration

Datasets

Data Insights

Custom Recipes

Feature Engineering

Modeling

Machine Learning Interpretability

Generative AI

Scoring on New Datasets

Transforming Datasets

Scoring Pipelines

Productionization

Clients

Monitoring and Logging

Security

Frequently Asked Questions

Appendices

References

Third-Party Notices

Translations