From Lab to Aisle: Scaling Deep Learning in Production Retail Systems
About this session
Retail is one of the most demanding proving grounds for enterprise AI high transaction volumes, real-time constraints, and a direct line to revenue and customer trust. This session looks at how deep learning architectures move from promising research to production systems that actually hold up at scale. We'll cover the core building blocks CNNs, LSTMs, attention mechanisms, and hybrid models and how they're applied to reduce stockouts, sharpen demand forecasting, and strengthen customer retention. We'll also look at computer vision in real-time systems like smart checkout, product recognition models handling massive SKU volumes, and transformer-based NLP turning customer sentiment into a usable business signal. Finally, we'll cover what it takes to bring this to the store level: edge computing and federated learning approaches that enable real-time analytics while protecting customer privacy. Drawing on experience leading AI-powered product initiatives at enterprise retail scale, this talk offers leaders a practical framework for evaluating where deep learning creates real value and the operational and architectural tradeoffs that matter once these systems leave the lab.
Speaker
Key takeaways
- Which deep learning architectures solve which retail problems — a practical framework for matching CNNs, LSTMs, attention mechanisms, and hybrid models to specific operational challenges like forecasting, stockouts, and retention.
- How production systems create real-time value — concrete examples of computer vision, product recognition, and NLP models operating at scale in live retail environments (smart checkout, SKU-level recognition, sentiment analysis).