Remove Python* Performance Barriers for Machine Learning


Online content and service providers like Netflix and Amazon have popularized the use of recommendation platforms/engines, that predict a user’s preferences based on historical ratings, collective user profiles and behavior. Collaborative filtering is the collective term for machine learning algorithms used by these engines to make personalized recommendations from extremely large datasets. Performance, accuracy and scalability are critical factors that determine the suitability of these systems in real time environments. This webinar highlights the significant performance speed-ups achieved by implementing multiple Intel tools and techniques for high performance Python on collaborative filtering methods benchmarked on the latest Intel® platforms. A combination of performance profiling with Intel® VTune™ Amplifier XE, accelerated machine learning algorithms in Intel® Data Analytics Acceleration Library and Intel® Distribution for Python*, and enhanced thread scheduling, showcase the individual strengths and combined computation power to drive performance on large scale machine learning workloads

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Product and Performance Information


Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804