AI in Retail: Developer Tools, APIs & MLOps Strategies

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