We are excited to announce our enhanced Model Monitoring feature is now live, and fully integrated within the H2O MLOps App v0.57 to provide a seamless and consistent user experience. Once a model is deployed, its performance can degrade over time as data in the serving environment can differ from the data the model was […]

Over and over again we heard from customers, “deep learning is cool, but it’s hard and time consuming.” They kept asking “could someone just make it easier?” In typical “Maker” fashion, you ask, we deliver, H2O Hydrogen Torch. H2O Hydrogen Torch is a new product that enables data scientists and developers to train and deploy […]

Machine learning (ML) models are only as good as the data fed into them. In tabular problems, the data is a collection of rows (samples) and columns (features). So, you could say that tabular ML models are only as good as the features fed into them. But how do you manage features? Can you share […]

We’re really excited to announce the H2O AI Feature Store – The only intelligent feature store in the market. We’ve been working on this for many months with our co-development partner: AT&T. This enabled us to build a first-of-its-kind platform that is designed to be enterprise-grade from day 1. It is built with best-of-breed technology […]

For almost a decade, H2O.ai has worked to build open source and commercial products that are on the leading edge of innovation in machine learning, from AutoML to Explainable AI. We are thrilled to announce the release of what we believe to be the future of AI Applications: H2O Wave. Wave is an open source, […]

We are excited to announce the new release of H2O Driverless AI with lots of improved features. Below are some of the exciting new features we have added: Version 1.6.1 LTS (April 18, 2019) – Available here Several improvements for MLI (partial dependence plots, Shapley values) Improved documentation for model deployment, time-series scoring, AutoViz and FAQs Version […]