H2O.ai brings Model Risk Management (MRM) to GenAI, combining enterprise-grade model evaluation with h2oGPTe and H2O Model Validation to make AI measurable, explainable, and …

Introduction to Machine Learning with H2O Tutorial. In this tutorial for the H2O platform, you will learn how to use H2O's GLM Random Forest, GBM Models, and grid search to tune …

H2O’s AutoML can be used for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit. H2O offers a …

H2O Open Source AutoML Train the best model in the least amount of time to save human hours, using a simple interface in R, Python, or a web GUI. Reduce the need for expertise in machine …

H2O MLOps provides a simple interface that enables end-to-end model management, 1-click deployments, automated scaling, and model monitoring that provides automated drift detection …

H2O MLOps allows Data Science teams to collaborate and maintain a central repository of models, irrespective of the ML framework used to train it. Teams are able to compare …

Sparkling Water allows users to combine the fast, scalable machine learning algorithms of H2O with the capabilities of Spark. Spark is an elegant and powerful general-purpose, open-source, …

Senior Solutions Engineer and Data Scientists at H2O.ai, Jon Farland, walks through machine learning concepts and worked-out examples with the H2O AI Cloud.

For binary classification problems, H2O uses the model along with the given dataset to calculate the threshold that will give the maximum F1 for the given dataset. This section describes how …

H2O supports training of supervised models (where the outcome variable is known) and unsupervised models (unlabeled data). Below we present examples of classification, …