Testing AI Models for the Real World: Robustness, Safety, and Edge Case Validation

The disconnect between test performance and production reality remains one of the most expensive problems in AI deployment - models that achieve impressive accuracy metrics during development often fail catastrophically when exposed to real-world data and user behavior. Through collaborations with multiple engineering teams, Karthikeyan developed a comprehensive testing framework that moves beyond traditional validation approaches to actively anticipate and prevent production failures. This methodology includes systematic adversarial testing to identify model breaking points, simulation of edge cases based on actual production incidents, and validation protocols that ensure graceful degradation rather than catastrophic failure. The framework has been tested across several high-stakes deployments, including financial services applications where errors directly impact revenue and healthcare systems where reliability is non-negotiable. Attendees will leave with immediately actionable techniques for stress-testing their models, a checklist for identifying hidden failure modes before deployment, and proven strategies for building robust AI systems that maintain predictable behavior even when encountering unexpected inputs. These practical tools have consistently reduced production incidents by 70% or more while significantly improving team confidence in deployment decisions.