H2O Driverless AI + Intel® DAAL Recipe

One of the critical segments in AI is Machine Learning because of its reliable solution in automating the learning process based on historical data. The AI ecosystem has rapidly grown within the past few years, enabling Data Scientists with various ease-of-use Machine Learning, Python-powered platforms. However, one of the challenges in using such Python-based solutions is its performance bottlenecks and lack of out-of-the-box optimizations for modern CPUs. To enable data science practitioners in classical Machine Learning applications, Intel provides Intel Data Analytics Acceleration Library (Intel® DAAL) which helps to obtain maximum performance for a wide range of CPU-based systems. Recently, Intel collaborated with H2O.ai to introduce the above Intel® DAAL optimizations into H2O Driverless AI via custom recipe files that deliver high performance by way of a simple extension of the core product. In this presentation, we will go over the core concepts of Intel® DAAL, and show how to build the enormously popular Gradient Boosting Tree model employing H2O-Intel DAAL recipes on AWS cloud. Rafael's Bio: Rafael Coss is a Community and Partner Maker at H2O.ai. Prior to joining H2O.ai, he was technical marketing and community Director and a developer advocate at Hortonworks. He was also the DataWorks Summit Program Co-Chair for the past 3 years. Prior to Hortonworks he was a Senior Solution Architect and Manager of IBM’s WW Big Data Enablement team. At IBM he was responsible for the technical product enablement for BigInsights and Streams. Preethi's Bio: Preethi Venkatesh is a Technical Consulting Engineering at Intel for AI products. Preethi is responsible for driving customer engagements on Machine and Deep Learning software adoption for Intel’s high-end processors, mainly Intel Xeon. Preethi primarily works on enabling Enterprises and Cloud Service Providers with Intel AI software.