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Company Overview of H2O.ai
H2O.ai develops an open source parallel processing prediction engine for machine learning and predictive analytics on big data. It offers H2O, an open source predictive analytics platform for data scientists, application developers, and business analysts who need in-memory machine learning for smarter applications. The company also provides marketing mix modeling, risk and fraud analysis, advertising technology, and customer intelligence solutions. H2O.ai was formerly known as 0xdata, Inc. and changed its name to H2O.ai in November 2014. The company was founded in 2011 and is based in Mountain View, California.
1185 Terra Bella Avenue
Mountain View, CA 94043
Founded in 2011
Key Executives for H2O.ai
Co-Founder and Chief Executive Officer
Head of Product & Data Science
Chief of Technology for Applications
Director of Public Relations
Compensation as of Fiscal Year 2016.
H2O.ai Key Developments
H2O.ai Integrates with Microsoft Azure HDInsight to Bring Robust AI platform to Enterprises
Apr 4 17
H2O.ai announced that its AI platform is now available on Microsoft Azure HDInsight. Customers can now use H2O.ai’s Sparkling Water solution on a HDInsight cluster along with Azure’s collection of cloud services. Together, the H2O team and Azure HDInsight team will integrate technologies to deliver enterprises the best business solutions and to build relationships within the open analytics community. Together with this combined offering of H2O on Azure HDInsight, customers can easily build data science solutions and run them at enterprise grade and scale. Azure provides the tools for a user to create a Data Science environment with underlying big data frameworks like Hadoop and Spark, while H2O’s technology brings a set of sophisticated, fully distributed algorithms to rapidly build and deploy highly accurate models at scale.
H2O.ai Launches Deep Water, Deep Learning for Enterprise
Mar 2 17
H2O.ai announced the release of its Deep Water product, a GPU-powered deep learning offering that’s giving Fortune 500 companies the ability to optimize business operations by analyzing and processing massive amounts of unstructured data, such as images, video, text and audio, at a much faster rate. Deep Water integrates GPU backends TensorFlow, MXNet and Caffe for broad adoption and ease of use. Most enterprise businesses have been blindsided by the unprecedented amount of data that’s materialized over the past decade. Deep Water ingests these enormous data sets and simplifies tasks for portfolio managers, claims adjusters, doctors and more. The tool’s unmatched computational power and predictive abilities fully automate processes like credit scoring and disease diagnosis. Other use cases include: Credit Risk and Lending - Redefine credit assessment using non-traditional data gathered through social media and instant loan approval with data analytics. Preventative Maintenance - Analyze images of homes and other property and provide insights on how to protect from potential damages. X-Ray Diagnosis - Automatically predict likelihood of specific diseases and ailments using x-ray scans. Predictive IT Traffic - Pattern analysis and prediction for Distributed Denial of Service (DDoS) detection and prevention and predictive maintenance of data center equipment. From a technical perspective, Deep Water’s multipronged solution bundles H2O's familiar interfaces from Python, R and H2O’s graphical UI (Flow) with the most popular deep learning GPU backends: TensorFlow, MXNet and Caffe. Moving forward, H2O.ai will continue to make Deep Water and its other tools even more automated using data from its enterprise customers, and its own community of open source users.
H2O.ai Unveils Sparkling Water 2.0
Jul 1 16
H2O.ai announced the availability of Sparkling Water 2.0. Sparkling Water 2.0 builds off the enormous popularity of Sparkling Water, H2O.ai's API for Apache Spark, with additional features and functionality. New features include the ability to interface with Apache Spark, Scala and MLlib via H2O.ai's Flow UI, build ensembles using algorithms from both H2O and MLlib and give Spark users the power of H2O's visual intelligence capabilities. Sparkling Water was designed to allow users to get the best of Apache Spark -- its elegant APIs, RDDs and multi-tenant Context -- along with H2O's speed, columnar-compression and fully-featured machine learning algorithms. Sparkling Water also allows for greater flexibility when it comes to finding the best algorithm for a given use case. Apache Spark's MLlib offers a library of efficient implementations of popular algorithms directly built using Spark. Sparkling Water empowers enterprise customers to use H2O algorithms in conjunction with, or instead of, MLlib algorithms on Apache Spark. Sparkling Water 2.0 includes the following improvements and functionality: Support for Apache Spark 2.0 and backwards compatibility with all previous versions. The ability to run Apache Spark and Scala through H2O's Flow UI. Support for the Apache Zeppelin notebook. H2O feature improvements and visualizations for MLlib algorithms, including the ability to score feature importance. Visual intelligence for Apache Spark. The ability to build Ensembles using H2O plus MLlib algorithms. The power to export MLlib models as POJOs (Plain Old Java Objects), which can be easily run on commodity hardware. A toolchain for building machine learning pipelines on Apache Spark. Production support for machine learning pipelines and the operationalization of MLlib through H2O scoring engines. Realtime machine learning for data products using Spark Streaming and H2O. Model and data governance through Steam. Bringing H2O's powerful data munging capabilities to Apache Spark. In addition to offering a greater degree of functionality and choice to Apache Spark, Sparkling Water 2.0 also delivers a new visualization component to MLlib. H2O.ai has recently built out a team  of data visualization experts whose sole focus is to make AI and machine learning algorithims easily consumable. The progress made by H2O.ai's visualization team will come to Apache Spark via Sparkling Water, allowing users to enjoy beautiful and easy to understand visualizations of algorithmic results.
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