Accelerating Data Science with RAPIDS - Keith Kraus

PyData DC 2018

Data science demands the interactive exploration of large volumes of data, combined with computationally intensive algorithms and analytics. Today, the computational limits of CPUs are being realized, and a new approach is needed. We will discuss how the GPU Open Analytics Initiative is breaking the compute barrier with GPU-accelerated libraries such as PyGDF and accelerating data science.
===
www.pydata.org

PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.

PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome!
00:10 Help us add time stamps or captions to this video! See the description for details.

Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Видео Accelerating Data Science with RAPIDS - Keith Kraus канала PyData