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.
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