AI Expo 2019 - Prakhar Mehrotra (Walmart Sr. Director of ML) - Part 2 of 4
Walmart - Prakhar Mehrotra (Sr. Director of ML, previously at Uber)
Walmart has huge scale: 0.5Trillion+ revenue, 3000+ stores with massive physical footprints, a massive global supply chain, Jet.com, Walmart.com, Shoes.com, Flipkart.com and it keeps growing.
The talk was focused on Walmart's application of ML, the contrasts of Uber-style surge pricing vs Walmart's fixed in-store pricing ("everyday low pricing"). A focus point was causality over correlation: understanding Walmart's customer and its supply chain (the Why?). Their primary domain was solving for shelf placement of inventory. Other interesting problems were inventory management, bridging the online and offline worlds (if we ship from warehouse, it's going to cost you X but if you pick up at this store where it's in stock it's X-3). The takeaway: Omni-channel shoppers are changing the customer profile for Walmart but adjusting for that isn't as simple as taking the .com purchase data and feeding it into the models.
The crux of the talk was a discussion of (NP-hard) Causality finding Bayesian Networks; Walmart worked around the NP-hardness by manually decomposing the Walmart supply chain into relatively independent units and their interrelations (suppliers, merchants, inventory, pricing, warehousing, transportation etc.). There was also a discussion about counterfactuals ("would sales have declined if I had not placed this item on promotion?"). A/B Testing is hard in the Walmart space because pricing is fixed in-store (and not per-shopper).
Fundamental datasets and metrics: Imagery, Similarity, Variants, Attributes, Classification, Quality, Scoring, Analysis
They're solving for:
1. Interventions (should I provide an offer to this user or intervene in another way?)
2. Associations (shelf placement, cart composition, suppliers)
3. Counterfactuals (what-if analysis)
Walmart's tech stack and algorithms:
Models/Algos: NN, Bayesian nets, Structural models
Tech Stack: Hive, Hadoop for big data, Teradata for medium-data, ETL systems, Jupyter for ad-hoc, hyper parameter optimization systems, CPU&GPU training, Scala and Python as primary languages for data scientists.
The final food for thought was: how fast will the online and offline worlds converge on dynamic pricing? What does that look like to the customer?
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