Speed scikit-learn Turnaround by 11x | Intel Software
33See how two lines of code can speed up Kmeans clustering, PCA, and silhouette machine learning algorithms in this customer segmentation application.
Production machine learning algorithms that power production applications often require low-latency turnaround on a range of CPUs and GPUs. Intel® Extension for Scikit-learn* dynamically patches popular scikit-learn algorithms with mathematically-equivalent versions that run optimally on Intel CPUs and GPUs.
Learn how to get started, illustrated using an end-to-end customer segmentation application that uses Kmeans clustering, principal component analysis (PCA), and silhouette scoring. This application shows an 11x speedup for the machine learning inference portion of the pipeline, running on a laptop. The same code is portable to any Intel CPU or GPU.
Technical article shown in the video: https://intel.ly/4bXrNJJ
Intel Extension for Scikit-learn: https://intel.ly/45xBeOe
Intel AI Software: https://intel.ly/44Yz5uJ
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Speed scikit-learn Turnaround by 11x | Intel Software