Retail is undergoing an AI-driven transformation — and developers are at the heart of it.

This session explores the developer toolkit powering retail’s AI revolution, from advanced APIs and MLOps pipelines to recommendation frameworks and real-time personalization engines.

Key insights covered:

  • Retail AI research grew from 12 papers (2000) to 847+ (2023) — a 21.3% CAGR
  • Modern AI systems process 150+ customer attributes in real time
  • Transformer-based models like BERT4Rec deliver 23–31% performance gains
  • Only 10% of AI implementations achieve ROI — we’ll uncover why
  • Generative AI in inventory systems processing 20–50 features
  • • Real-time personalization APIs achieving 75% accuracy
  • Forecasting improvements of 30–50% with scalable architectures

Technical focus areas:

  • MLOps pipelines for scalable retail AI
  • API design patterns for recommendation engines
  • Data preprocessing tools for customer attribute management
  • Model deployment strategies for real-time personalization
  • Monitoring frameworks for production AI systems

Whether you’re a developer, architect, or retail innovator, you’ll leave with actionable strategies to build production-ready AI systems that deliver business impact at scale.