With only one U.S. state without a Walmart supercenter — and over 4,600 stores across the country — the retail giant’s prediction analytics work with data on an enormous scale.
Grant Gelven, a machine learning engineer at Walmart Global Tech, joined NVIDIA AI Podcast host Noah Kravitz for the latest episode of the AI Podcast.
Gelven spoke about the big data and machine learning methods making it possible to improve everything from the customer experience to stocking to item pricing.
Gelven’s most recent project has been a dynamic pricing system, which reduces excess food waste by pricing perishable goods at a cost that ensures they’ll be sold. This improves suppliers’ ability to deliver the correct volume of items, the customers’ ability to purchase, and lessens the company’s impact on the environment.
The models that Gelven’s team work on are extremely large, with hundreds of millions of parameters. They’re impossible to run without GPUs, which are helping accelerate dataset preparation and training.
The improvements that machine learning have made to Walmart’s retail predictions reach even farther than streamlining business operations. Gelven points out that it’s ultimately helped customers worldwide get the essential goods they need, by allowing enterprises to react to crises and changing market conditions.
Key Points From This Episode:
- Gelven’s goal for enterprise AI and machine learning models isn’t just to solve single use case problems, but to improve the entire customer experience through a complex system of thousands of models working simultaneously.
- Five years ago, the time from concept to model to operations took roughly a year. Gelven explains that GPU acceleration, open-source software, and various other new tools have drastically reduced deployment times.
“Solving these prediction problems really means we have to be able to make predictions about hundreds of millions of distinct units that are distributed all over the country.” — Grant Gelven [3:17]
“To give customers exactly what they need when they need it, I think is probably one of the most important things that a business or service provider can do.” — Grant Gelven [16:11]
You Might Also Like:
Focal Systems Brings AI to Grocery Stores
CEO Francois Chaubard explains how Focal Systems is applying deep learning and computer vision to automate portions of retail stores to streamline store operations and get customers in and out more efficiently.
Credit Check: Capital One’s Kyle Nicholson on Modern Machine Learning in Finance
Kyle Nicholson, a senior software engineer at Capital One, talks about how modern machine learning techniques have become a key tool for financial and credit analysis.
HP’s Jared Dame on How AI, Data Science Are Driving Demand for Powerful New Workstations
Jared Dame, Z by HP’s director of business development and strategy for AI, data science and edge technologies, speaks about the role HP’s workstations play in cutting-edge AI and data science.
Tune in to the AI Podcast
Get the AI Podcast through iTunes, Google Podcasts, Google Play, Castbox, DoggCatcher, Overcast, PlayerFM, Pocket Casts, Podbay, PodBean, PodCruncher, PodKicker, Soundcloud, Spotify, Stitcher and TuneIn. If your favorite isn’t listed here, drop us a note.
Make the AI Podcast Better
Have a few minutes to spare? Fill out this listener survey. Your answers will help us make a better podcast.