Production AI Is a Systems Problem: How ML Breaks at Scale and How to Prevent It

Machine learning systems rarely fail because of bad models. They fail because they are treated as isolated components instead of what they really are: distributed systems.

In this talk, we’ll look at production AI through a systems engineering lens. We’ll explore the failure modes that only appear once ML systems are deployed at scale—partial outages, data inconsistencies, version skew, silent performance regressions, and feedback loops that break assumptions over time.

Drawing on real-world production patterns (presented in a generalized and anonymized way), I’ll show how applying classic distributed systems principles—such as graceful degradation, backpressure, observability, and clear contracts—can dramatically improve the reliability and maintainability of AI systems.

This session focuses less on model architecture and more on how ML systems interact with data pipelines, APIs, infrastructure, and downstream services. Attendees will leave with a practical mental model for designing, operating, and scaling AI systems that survive real-world conditions, not just demos.