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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