Where can I listen to this podcast?

This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.

Does this podcast accept guests?

Yes, this podcast regularly features guests.

RAPIDSFire

Claim This Podcast

by RAPIDS

4.9(14 reviews)

15 episodes

Updated Daily

Accepts GuestsHas Sponsors

Podcast Overview

Follow the RAPIDSFire podcast for a fresh take on data science. Hear from revolutionaries transforming data science on GPUs for scientific research, higher education, and the broader enterprise. Talks with open-source software maintainers, Kaggle grandmasters, practitioners, CUDA experts and many others keep you up-to-date on the most exciting developments. Let's discuss how to make your work better and faster. Hosted by Data Scientist Paul Mahler. Join the conversation and send feedback on Twitter @rapidsai

Language

🇺🇲

Publishing Since

12/8/2020

Visit Website View on Apple Podcasts RSS Feed

1 verified contact email on file for RAPIDSFire

Pitch yourself as a guest, propose sponsorships, or reach out directly to the host.

See contact details

Recent Episodes

October 14, 2021

RAPIDSFire Sports Spectacular 1 - Sam Moss and Cameron Weinert of Every Day is Saturday

I talk with Sam Moss and Cameron Weinert about using data science to predict college football. We talk about feature engineering, following your passing, the role of analytics in sports and sports fandom, how to be an intelligent consumer of data science as a non-data scientist, and a lot more.  <a href="https://open.spotify.com/show/1KppipAhjcwJatAW8eO4PZ">Everyday is Saturday on Spotify.</a>  <a href="https://podcasts.apple.com/us/podcast/every-day-is-saturday-predicting-college-football/id1143962052">Everyday is Saturday on Apple.</a>  <a href="https://twitter.com/cfbneuralnet">Sam on Twitter.</a>  Raw data on college football here at <a href="//collegefootballdata.com">collegefootballdata.com</a>

October 5, 2021

Marlene Mhangami on Python, Pivots, and Personal Growth (and RAPIDS on Windows)

We talk with Marlene Mhangami, a director and chair of the Python Software Foundation, co-founder of coding education non-profit ZimboPy, someone that took a huge career pivot from pre-med to software engineering, and one of the folks that helped bring RAPIDS to Windows. We talk about changing careers, creativity and confidence in tech, and of course RAPIDS on Windows.  Marlene's home page https://marlenemhangami.com/ Marlene's blog post about RAPIDS on Windows https://medium.com/rapids-ai/running-rapids-on-microsoft-windows-10-using-wsl-2-the-windows-subsystem-for-linux-c5cbb2c56e04 Tutorial on using RAPIDS on Windows via WSL2 https://www.youtube.com/watch?v=jnEd3IDsF-I ZimboPy on github https://github.com/ZimboPy

September 8, 2021

Even Oldridge on Tabular Deep Learning and the Future of Recommender Systems

This week we’re joined by Even Oldridge, Senior Manager, RecSys Platform Team at NVIDIA. We talk about Tabular Deep Learning, NVMerlin, how bookstores aren’t like recommender systems, his team’s recent repeat win in the ACM Recsys Challenge, the future of recommender systems and more. NVIDIA Merlin on the NVIDIA Developer Blog <a href="https://developer.nvidia.com/blog/tag/merlin/">https://developer.nvidia.com/blog/tag/merlin/</a> NVIDIA Merlin blogs on Medium <a href="https://medium.com/nvidia-merlin">https://medium.com/nvidia-merlin</a> Merlin on Github <a href="https://github.com/NVIDIA-Merlin/Merlin">https://github.com/NVIDIA-Merlin/Merlin</a> NVTabular Blogs <a href="https://developer.nvidia.com/blog/tag/nvtabular/">https://developer.nvidia.com/blog/tag/nvtabular/</a> NVTabular on Github <a href="https://github.com/NVIDIA/NVTabular">https://github.com/NVIDIA/NVTabular</a> REES46 data set mentioned toward the end of the podcast <a href="https://rees46.com/en/datasets">https://rees46.com/en/datasets</a>

15 total episodes available

Deep-dive analytics for RAPIDSFire

Frequently asked questions

Have a different question and can't find the answer you're looking for? Reach out to our support team by sending us an email and we'll get back to you as soon as we can.

What is RAPIDSFire?: Follow the RAPIDSFire podcast for a fresh take on data science. Hear from revolutionaries transforming data science on GPUs for scientific research, higher education, and the broader enterprise. Talks with open-source software maintainers, Kaggle grandmasters, practitioners, CUDA experts and many others keep you up-to-date on the most exciting developments. Let's discuss how to make your work better and faster. Hosted by Data Scientist Paul Mahler.

Join the conversation and send feedback on Twitter @rapidsai
How often does this podcast release new episodes?: This podcast updates daily.
Where can I listen to this podcast?: This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
Does this podcast accept guests?: Yes, this podcast regularly features guests.

Legal Disclaimer

Pod Engine is not affiliated with, endorsed by, or officially connected with any of the podcasts displayed on this platform. We operate independently as a podcast discovery and analytics service.

All podcast artwork, thumbnails, and content displayed on this page are the property of their respective owners and are protected by applicable copyright laws. This includes, but is not limited to, podcast cover art, episode artwork, show descriptions, episode titles, transcripts, audio snippets, and any other content originating from the podcast creators or their licensors.

We display this content under fair use principles and/or implied license for the purpose of podcast discovery, information, and commentary. We make no claim of ownership over any podcast content, artwork, or related materials shown on this platform. All trademarks, service marks, and trade names are the property of their respective owners.

While we strive to ensure all content usage is properly authorized, if you are a rights holder and believe your content is being used inappropriately or without proper authorization, please contact us immediately at hey@podengine.ai for prompt review and appropriate action, which may include content removal or proper attribution.

By accessing and using this platform, you acknowledge and agree to respect all applicable copyright laws and intellectual property rights of content owners. Any unauthorized reproduction, distribution, or commercial use of the content displayed on this platform is strictly prohibited.