Traditional feature flags are binary decisions, but what if they could learn and adapt? In this session, we'll explore how to build an intelligent feature management system that uses machine learning to make data-driven deployment decisions. You'll learn how to combine PyTorch, Prometheus, and Kubernetes to create a system that automatically controls feature rollouts based on real-time metrics and learned patterns.

Through live demonstrations and practical examples, you'll discover how to evolve your feature management from simple toggles to an intelligent system that learns from your users and system metrics. By the end of this session, you'll have the knowledge to implement ML-powered progressive delivery in your own organization. This talk is for platform engineers, DevOps practitioners, and ML engineers who want to take their feature management to the next level. Attendees should have basic familiarity with Kubernetes and Python. Experience with feature flags or ML is helpful but not required. You'll leave understanding how to combine these technologies to create more intelligent deployment strategies.