Driverless AI: Fast, Accurate, Interpretable AI
H2O.ai recently launched Driverless AI, which speeds up data science workflows by automating feature engineering, model tuning, ensembling, and model deployment.
By Rosalie Bartlett, Director of Community. Sponsored Post.
At H2O.ai, we’re excited to have recently launched Driverless AI, which speeds up data science workflows by automating feature engineering, model tuning, ensembling, and model deployment. If you’d like to try Driverless AI, please feel free to request a 28-day trial.
Driverless AI turns Kaggle-winning recipes into production-ready code and is specifically designed to avoid under or overfitting, data leakage or improper model validation.
With Driverless AI, you can train and deploy modeling pipelines with a few clicks from the GUI. You can also use the client/server API through a variety of languages such as Python, Java, C++, go, C# and many others.
To speed up training, Driverless AI uses highly optimized C++/CUDA algorithms to take full advantage of the latest compute hardware. For example, Driverless AI runs orders of magnitudes faster on the latest Nvidia GPU supercomputers on Intel and IBM platforms, both in the cloud or on premise.
Driverless AI also allows for statistically rigorous automatic data visualization and interactive model interpretation with reason codes and explanations in plain English.
If you’d like to be notified about our upcoming “Driverless AI - Intro + Interactive Hands-on Lab” webinar, please click here.
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