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.
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]
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