Overview

Build and Productionalization Machine Learning Models on Big Data

H2O’s core code is written in Java. Inside H2O, a Distributed Key/Value store is used to access and reference data, models, objects, etc., across all nodes and machines. The algorithms are implemented on top of H2O’s distributed Map/Reduce framework and utilize the Java Fork/Join framework for multi-threading. The data is read in parallel and is distributed across the cluster and stored in memory in a columnar format in a compressed way. H2O’s data parser has built-in intelligence to guess the schema of the incoming dataset and supports data ingest from multiple sources in various formats.

Features & Benefits

Fast & Accurate

The responsiveness of in-memory processing and the ability to run fast serialization between nodes and clusters are combined—so you can support the size requirements of your large data sets.

Scalable

Fine-Grain distributed processing on big data at speeds up to 100x faster is done with fine-grain parallelism, which enables optimal efficiency, without introducing degradation in computational accuracy.

Easy to Use

Get started quickly using H2O’s intuitive web-based Flow GUI or familiar programming environments. Deploy POJOs and MOJOs, score new data for accurate predictions in any environment.

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How It works

Open source, in-memory, distributed, fast, and scalable

H2O makes it possible for anyone to easily apply machine learning and predictive analytics to solve today’s most challenging business problems.

Example One

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Example Two

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Enterprise Support

When AI becomes mission critical for enterprise success, H2O.ai is there to help. H2O Enterprise Support provides the services you need to optimize your investments in people and technology to deliver on your AI vision including training, technical support for data science and operations, a dedicated account manager and direct enhancement requests.

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