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Speed scikit-learn Turnaround by 11x | Intel Software [Video]

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Speed scikit-learn Turnaround by 11x | Intel Software

See 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

About Intel Software:
Intel® Developer Zone is committed to empowering and assisting software developers in creating applications for Intel hardware and software products. The Intel Software YouTube channel is an excellent resource for those seeking to enhance their knowledge. Our channel provides the latest news, helpful tips, and engaging product demos from Intel and our numerous industry partners. Our videos cover various topics; you can explore them further by following the links.

Connect with Intel Software:
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Speed scikit-learn Turnaround by 11x | Intel Software

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