H2O AI Hybrid Cloud helps data scientists accelerate the model building process with advanced automatic feature engineering, automatic hyper-parameter tuning, automatic algorithm selection, and automatic model validation. H2O’s automated machine learning was designed by top data scientists in the world, and produces highly accurate and robust models for structured data, text, image, video, and time-series data.

Automatic Feature Engineering

H2O AI Hybrid Cloud automates the entire feature engineering process, significantly accelerating one of the most time consuming data science tasks. H2O tests and detects the relevant features, finds important interactions between those features, and derives completely new features from the data. With the newly derived features, H2O’s technology automatically recalculates and iterates until the best predictors have been created and ranked for importance.

AutoML for Any Data

H2O AI Hybrid Cloud enables users to rapidly create world-class models for not only tabular data, but also text, image, video, and time-series data. H2O automatically selects the best algorithm, tunes the model, and provides data scientists and developers with a model leaderboard to continually challenge and test the champion models.

“Thanks to this powerful AutoML, I only have to worry about knowing my data, creating more than 30 experiments in an hour, it facilitates the proofs of concepts during my research”

Adolfo Vera, Solution Architect and Data scientist

Structured/Tabular Data

Tabular and structured data is often the most common and valuable data to organizations, such as ERP and CRM. This information can be used to predict maintenance failures, detect fraud, identify customers who are likely to churn, and much more. H2O’s AutoML performs as well on tabular data sets as the leading deep learning methods available.

Natural Language Processing (NLP)

Companies are aggressively looking at text data to solve business problems such as streamlining customer service, personalizing marketing, tracking brand or product sentiment, classifying documents, and tagging content. H2O AI Hybrid Cloud helps data scientists and developers rapidly build NLP models by automatically converting text strings into features using techniques like BERT, TFIDF, CNN, and GRU. Similar to automatic feature engineering, H2O automates virtually all of the text pre-processing required to solve the most complicated NLP problems.

H2O AI Hybrid Cloud also includes state-of-the-art PyTorch BERT transformers. With advanced NLP techniques, users can also process larger text blocks and build models using all available data and text.

Time Series

Time-Series problems are one of the most ubiquitous analytics problems facing companies today, from revenue forecasting, to FP&A, to dynamic pricing. H2O AI Hybrid Cloud supports a robust universe of time-series modeling methods as well as unique automatic feature engineering specifically for time-series problems, enabling users to quickly identify what temporal features matter in the data. H2O also supports modeling methods such as SIERD models, which specialize in forecasting the spread of contagious diseases, such as COVID-19.


Organizations are rapidly and increasingly looking to use image and video data to improve security, identify manufacturing errors, optimize retail layouts, and much more. H2O AI Hybrid Cloud automatically creates image and video models using over 30 pre-trained image transformers and models including (SE)-ResNe(X)ts, DenseNets, MobileNets, EffientNets, and Inceptions. Images can be processed alone or as part of datasets that include other data types, such as tabular and text data.

Train Large Datasets Fast

H2O AI Hybrid Cloud is optimized to work ith the latest Nvidia GPUs, IBM Power 9 and Intel x86 CPUs. It supports distributed computing and rapidly trains models from TBs of data.

Support for Leading ML Libraries, Recipes, and Extension

H2O AI Hybrid Cloud supports popular ML libraries including H2O-3, XGBoost, Tensorflow, and PyTorch, as well as over 100 pre-coded experiments and recipes. In addition, customers can extend H2O AI Hybrid Cloud’s modeling capabilities by using their own libraries and recipes.

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