Drive Innovation & Performance into Your scikit-learn* Machine Learning Tasks

As a general purpose machine learning library for classification, regression, and clustering algorithms, scikit-learn* has many real-world applications. For example, support-vector machines (SVMs), random decision forests, gradient boosting, K-means clustering, and the density-based spatial clustering of applications with noise (DBSCAN), which is a data-clustering algorithm.

Unfortunately, the scikit-learn library by Python* is inherently slow on hardware without additional software optimizations.

Enter the Intel® Extension for Scikit-learn*, part of the Intel® AI Analytics Toolkit. In this webinar, AI technical consulting engineer Rachel Oberman discusses this library, including:

• How it can speed up scikit-learn in just two lines of code, delivering at least 2x better performance
• How to improve scikit-learn memory access
• An overview to get started and a census use case

• Get the Intel Extension for Scikit-learn as part of the AI Frameworks and Tools—a specialized set of tools and frameworks to accelerate end-to-end data science pipelines.
• Get the stand-alone version of Intel Extension for Scikit-learn.

• Sign up for an Intel® Tiber™ AI Cloud account—a free development sandbox with access to the latest Intel hardware and oneAPI software.
• Explore oneAPI including developer opportunities and benefits.
• Subscribe to Code Together— an interview series that explores the challenges at the forefront of cross-architecture development. Each biweekly episode features industry VIPs who are blazing new trails through today’s data-centric world. Available wherever you get your podcasts.