Today we’re joined by Paul Mahler, senior data scientist and technical product manager for machine learning at NVIDIA.
Subscribe: iTunes / Google Play / Spotify / RSS
In our conversation, Paul and I discuss NVIDIA’s RAPIDS open source project, which aims to bring GPU acceleration to traditional data science workflows and machine learning tasks. We dig into the various subprojects like cuDF and cuML that make up the RAPIDS ecosystem, as well as the role of lower-level libraries like mlprims and the relationship to other open-source projects like Scikit-learn, XGBoost and Dask.
Thanks to our Sponsor!
This week’s shows are drawn from some of the great conversations I had at the recent NVIDIA GPU Technology Conference, and they’re brought to you by Dell.
If you caught my tweets from GTC, you may already know that one of the announcements this year was a new reference architecture for Data Science Workstations, powered by high-end GPUs and accelerated software such as NVIDIA’s RAPIDS. Dell was among the key partners showcased during the launch, and offers a line of workstations designed for modern ML and AI workloads.
To learn more about Dell Precision workstations, and some of the ways they’re being used by customers in industries like Media and Entertainment, Engineering and Manufacturing, Healthcare and Life Sciences, Oil and Gas, and Financial services, visit Dellemc.com/Precision.
Mentioned in the Interview
- Check out the rest of our GTC 2019 Series!
- Check out all of our great series from 2018 at the TWiML Presents: Series page!
- TWiML Online Meetup
- Register for the TWiML Newsletter
“More On That Later” by Lee Rosevere licensed under CC By 4.0