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® oneAPI 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 Software
- Get the Intel Extension for Scikit-learn as part of the Intel oneAPI AI Analytics Toolkit—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® DevCloud for oneAPI 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.