H2O AI and Intel
H2O and Intel collaborated to accelerate H2O machine learning on Intel architecture. Through a co-innovation effort called Project Blue Danube, the companies optimized H2O algorithms and libraries, including XGBoost, for Intel Xeon Scalable processors, and brought H2O Driverless AI, our AutoML, to customers on Intel platforms. Organizations can run predictive AI on the CPU infrastructure they already own.
Partnership overview
Accelerating H2O on Intel architecture
H2O and Intel joined forces on a major joint effort named Project Blue Danube. The initiative accelerates H2O machine learning algorithms and libraries on Intel platforms, including optimizing algorithms such as XGBoost on Intel Xeon. H2O and Intel set out to give organizations a scalable, high-performance and secure data science platform that accelerates their data science workflows on the world's most pervasive platform.
Machine learning at scale
H2O's CPU-accelerated machine learning algorithms, powered by Intel, are tuned for optimal CPU performance. Organizations can apply data science techniques to critical problems quickly, easily and at scale. Deployment is simple, and every machine learning model can be interpreted transparently, with high accuracy.
H2O Driverless AI, our AutoML, applies the techniques of expert data scientists in an easy-to-use application. Data scientists finish projects faster with automatic machine learning on Intel computing power, doing in minutes tasks that used to take months.
Faster time to market
Intel and H2O provide an integrated, validated AI platform with the tools and services to operationalize AI and shorten time to market. H2O algorithms and tools come bundled and optimized with Intel AI technology.
Customers can optimize price/performance while cutting infrastructure evaluation time and the integration work needed to build an effective AI platform. Optimized libraries and frameworks raise performance and reduce programming complexity. Preloaded runtime environments and libraries improve performance and simplify deployment.