The Art of Building Practical AI/ML Systems

A fraud model shows 95% accuracy in the lab, yet months later it floods operations with false alerts. The model didn’t fail—the production environment did. Many ML systems break after deployment due to data drift, fragile pipelines, misaligned metrics, and real-world constraints that notebooks ignore. This session shares a practical playbook for building AI that works beyond prototypes. Learn how to start with business decisions, choose metrics that reflect cost trade-offs, design reliable data pipelines, monitor drift, plan deployment early, and favor interpretable, scalable models over unnecessary complexity. Walk away with actionable strategies to ship production-ready ML systems that deliver lasting business impact, not just great offline accuracy.