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 highest predictive accuracy, comparable to expert data scientists, but in much shorter time thanks to end-to-end automation. Driverless AI also offers automatic visualizations 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.

Driverless AI runs on commodity hardware. It was also specifically designed to take advantage of graphical processing units (GPUs), including multi-GPU workstations and servers such as IBM’s Power9-GPU AC922 server and the NVIDIA DGX-1 for order-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 https://www.infoworld.com/article/3236048/machine-learning/review-h2oai-automates-machine-learning.html.

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 H2O.ai Community Slack workspace in the #driverlessai channel. If you have not signed up for the H2O.ai Community Slack workspace, you can do so here: https://www.h2o.ai/community/.

Scoring on New Datasets

Transforming Datasets


Third-Party Notices