H2O Driverless AI makes data scientists more productive by automating some of the most challenging and productive tasks in applied machine learning such as feature engineering, model selection, model tuning and ensembling, as well as model interpretability and production deployment. Driverless AI turns Kaggle-winning grandmaster recipes into production-ready code (Java and C++), and is specifically designed to avoid common mistakes such as under- or overfitting, data leakage or improper model validation, which are some of the hardest challenges in data science. Domain experts and advanced data scientists can write their own recipes and seamlessly extend Driverless AI with their favorite tools from the rich ecosystem of open-source data science and machine learning libraries.
Join us on Tuesday, July 9th at 2:00 PM EDT, to hear from Arno Candel, CTO at H2O.ai and Dan Darnell, VP of Product Marketing at H2O.ai, as they walk you through the latest innovations available in H2O Driverless AI 1.9.0.
This meetup gives an overview of the new features in H2O Driverless AI 1.9.0, including:
– Time-Series techniques for modeling trends including epidemic SEIRD model
– Insurance use case modeling with ZeroInflated models
– Computer Vision with TensorFlow Deep Learning models
– Language models with PyTorch BERT Deep Learning models
– Preview of Collaboration, Model Storage and Model Ops, coming in 2.0 LTS
We will provide live demonstrations on example use cases to show the benefits of these new capabilities.