H2O Driverless AI is an artificial intelligence (AI) platform that 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.
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 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, please see https://www.h2o.ai/driverless-ai/.
For a third-party review, please see https://www.infoworld.com/article/3236048/machine-learning/review-h2oai-automates-machine-learning.html.
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.
- Running an Experiment
- Interpreting a Model
- Interpret this Model Button
- Model Interpretation on Driverless AI Models
- Model Interpretation on External Models
- The Model Interpretation Page
- Global and Local Feature Importance
- Decision Tree Surrogate Model
- Partial Dependence and Individual Conditional Expectation (ICE)
- General Considerations
- Viewing Explanations
- Score on Another Dataset
- Transform Another Dataset
- The Python and MOJO Scoring Pipelines
- Viewing Experiments
- Visualizing Datasets
- Launching H2O Flow