Capital One is upping the ante with AI at its Center for Machine Learning, where one of our latest AI Podcast guests works as a senior software engineer.
Kyle Nicholson joined AI Podcast host Noah Kravitz to discuss how the company is performing data science at scale.
Nicholson also hosted a GTC Digital session on the topic with his colleague Mike McCarty, director of software engineering.
Using open source tools like Dask and RAPIDS, Capital One has scaled out to 96 NVIDIA GPUs and 2TB of GPU memory.
Key Points From This Episode:
- Nicholson dove into the details of how GPUs accelerate data science across the enterprise. For a training session on one particular model, Capital One achieved a 100x speedup for a 40GB dataset, with a 97 percent reduction in training cost, by scaling out over multiple GPUs.
- He also weighed in on the benefits and drawbacks of using open source tools in an enterprise environment. Capital One has careful security in place so that they can contribute their learnings without accidentally sharing intellectual property.
Learn more about AI at Capital One.
“With Dask you can really load data into memory, and operate on it quickly. You can know more than you ever could before.” — Kyle Nicholson [4:42]
“The whole point of being part of an open source community is that you don’t just take from the community but that you also give back what you’ve done” — Kyle Nicholson [16:30]
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