Today we’re joined by Pat Woowong, principal engineer in the applied machine intelligence group at The Home Depot.
Subscribe: iTunes / Google Play / Spotify / RSS
We discuss a project that Pat recently presented at the Google Cloud Next conference which used machine learning to predict shelf-out scenarios within stores. We dig into the motivation for this system and how the team went about building it, including what type of models ended up working best, how they collected their data, their use of kubernetes to support future growth in the platform, and much more.
Sign up for our Newsletter!
Be sure to sign up for our weekly newsletter. We recently shared a write up detailing the ML/AI Job Board we’re working on, and got a ton of encouragement and interest. To make sure you don’t miss anything, head over to twimlai.com/newsletter to sign up.
Mentioned in the Interview
- Video: The Value Proposition for Using ML in Brick-and-Mortar Retail Stores: Home Depot (Cloud Next ’18)
- TWiML Presents: Series page
- TWiML Events Page
- TWiML Meetup
- TWiML Newsletter
“More On That Later” by Lee Rosevere licensed under CC By 4.0