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 pure 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.
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.
- Why Driverless AI?
- Key Features
- Flexibility of Data and Deployment
- NVIDIA GPU Acceleration
- Automatic Data Visualization (Autovis)
- Automatic Feature Engineering
- Automatic Model Documentation
- Time Series Forecasting
- NLP with TensorFlow
- Automatic Scoring Pipelines
- Machine Learning Interpretability (MLI)
- Automatic Reason Codes
- Custom Recipe Support
- Supported Algorithms
- Driverless AI Workflow
- Installing and Upgrading Driverless AI
- Using the config.toml File
- Environment Variables and Configuration Options
- Enabling Data Connectors
- Configuring Authentication
- Enabling Notifications
- Launching Driverless AI
- The Datasets Page
- Diagnosing a Model
- Project Workspace
- Scoring Pipelines Overview
- Which Pipeline Should I Use?
- Driverless AI Standalone Python Scoring Pipeline
- Driverless AI MLI Standalone Python Scoring Package
- MOJO Scoring Pipelines
- What’s Happening in Driverless AI?
- Data Sampling
- Driverless AI Transformations
- Internal Validation Technique
- Missing and Unseen Levels Handling
- Time Series in Driverless AI
- NLP in Driverless AI
- The Python Client
- Installing the Python Client
- Driverless AI: Credit Card Demo
- Driverless AI - Training Time Series Model
- Driverless AI - Time Series Recipes with Rolling Window
- Driverless AI NLP Demo - Airline Sentiment Dataset
- Time Series Analysis on a Driverless AI Model Scoring Pipeline
- Driverless AI Autoviz Python Client Example
- The R Client
- Tips ‘n Tricks