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 • 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.

AI in retail, retail AI development, retail APIs, MLOps for retail, AI recommendation systems, BERT4Rec in retail, retail personalization AI, customer data AI tools, generative AI in retail, AI forecasting retail, AI deployment strategies, retail AI frameworks, AI-driven retail transformation

#RetailAI #AIDevTools #MLOps #Personalization #RecommendationSystems #GenerativeAI #RetailInnovation