Anaconda Collaborates with Intel to Improve Speed and Scale for Machine Learning Workflows
With easier access to optimized versions of key libraries like Scikit-learn, Anaconda and Intel are improving the data science experience.
AUSTIN, Texas, June 29, 2021 -- Anaconda today announced further developments in their ongoing collaboration with Intel, as well as plans to continue expanding their strategic partnership for data science into the future. The companies are working together to build critical open-source data science packages, optimized for Intel technology, to make fast and scalable machine learning accessible for practitioners everywhere.
“We’re thrilled to continue our work with Intel to equip data scientists with the tools they need to build and deploy models efficiently,” said Peter Wang, CEO, and co-founder of Anaconda. “Data science and machine learning workloads account for a growing portion of business computing costs, and we’re happy to serve our community by making Intel-optimized package options available.”
To deliver the best possible user experience for data science practitioners, packages and libraries optimized for Intel oneAPI Math Kernel 图书馆, including NumPy and SciPy, are now easily accessible in Anaconda’s curated package repository. Built on top of these libraries is the machine learning module Scikit-learn, one of the most popular libraries with data scientists. Intel Extension for Scikit-learn, which uses Intel oneAPI Data Analytics 图书馆 and its convenient Python API daal4py, makes machine learning algorithms in Python an average of 27 times faster in training and 36 times faster during inference and optimizes scikit-learn’s application performance1. Anaconda users can now access the speed improvements of Intel Extension for Scikit-learn directly from Anaconda’s repository.
As the partners continue to collaborate, users can expect additional optimizations to speed up and scale out more aspects of the data science 工具包.
“We look forward to working with Anaconda to speed discovery by making fast and reliable machine learning available to researchers around the globe,” said Arijit Bandyopadhyay, CTO - Enterprise Analytics & AI, Head of Strategy - Enterprise & Cloud, Data Platforms Group at Intel Corporation. “Performance is key when it comes to machine learning, and with this partnership, practitioners can leverage the innovation of open source on powerful hardware, which helps accelerate timelines and increase value.”
You can find additional details about our partnership and the technical information on Anaconda’s blog.
With more than 25 million users, Anaconda is the world’s most popular data science platform and the foundation of modern machine learning. We pioneered the use of Python for data science, champion its vibrant community, and continue to steward open-source projects that make tomorrow’s innovations possible. Our enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness the power of open-source for competitive advantage, groundbreaking research, and a better world.
Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries.
 Performance varies by use, configuration and other factors. Performance results are based on Anaconda testing as of dates shown in configurations and may not reflect all publicly available updates. See blog post for configuration details. Past performance doesn't necessarily indicate future results. Results may vary.