Sparkling Water: Gives access to H2O algorithms developed from the ground up for distributed computing and for both supervised and unsupervised approaches. Drives computation from Scala, R, or Python and use the H2O Flow UI, providing an ideal machine learning platform for application developers. Easy to deploy POJOs and MOJOs to deploy models for fast and accurate scoring in any environment, including very large models.
H2O Driverless AI: Allows automatic feature engineering, brings your own models to feature, used for image and natural language processing.
Enterprise Puddle: The H2O.ai open source and the Driverless AI platforms allow data scientists to deploy models in any cloud. Deploy the machine learning model as a RESTful or serverless endpoint for real-time scoring directly in the cloud. Model building on H2O comes with limitless flexibility for the data teams. It is capable of end-to-end data science in your cloud – everything from feature engineering, machine learning, interpretability to deployment. Leverage the high availability, scalable storage, compute, and memory of public clouds to support your production workloads. Use H2O with GPUs and CPUs to train and score models in your cloud.