Presentation: Building Evals for AI Adoption: From Principles to Practice
Evaluation debt is silently breaking production AI systems at scale. Here's how Twitter, Walmart, and Netflix are fixing it.
Why it matters
As AI adoption moves from pilots to production, engineering leaders are discovering that traditional metrics mask semantic failures. This presentation offers a diagnostic framework for eliminating evaluation debt—a critical governance gap that could determine whether enterprise AI investments deliver ROI or fail silently.
The key facts
11 to knowEvaluation debt identified as hidden risk in production AI systems
Five-layer evaluation stack framework spanning infrastructure and UX
Traditional metrics fail to catch silent semantic failures in modern architectures
Diagnostic maturity model for engineering leaders
Case studies from Twitter, Walmart, Netflix
Focus on evaluation governance and operational risk mitigation
Five-layer evaluation stack framework (infrastructure to UX)
Evaluation debt identified as production risk across Twitter, Walmart, Netflix
Traditional metrics identified as inadequate for modern architectures
Diagnostic maturity model for engineering leaders provided
Focus on silent semantic failures in deployed systems
Go to the source
InfoQ AI/MLinfoq.com
Publisher excerpt: Mallika Rao discusses the hidden risk of evaluation debt in production AI systems, drawing on her experience at Twitter, Walmart, and Netflix. She explains why traditional metrics fail modern architectures, breaks down a five-layer evaluation stack spanning infrastructure and UX, and shares a…