Presentation: Building Evals for AI Adoption: From Principles to Practice
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 diagnostic maturity model to help engineering leaders eliminate silent semantic failures. By Mallika Rao
As AI models move from research to widespread production, the critical need for robust and scalable evaluation systems is becoming abundantly clear, especially in enterprise settings.
The shift from theoretical AI performance to actual production reliability is a major bottleneck; effective evaluation is crucial for secure and efficient AI adoption and preventing costly failures.
The focus in AI development is expanding from model training to include comprehensive, practical evaluation frameworks that address the complexities of real-world deployment and UX integration.
- · AI evaluation tool developers
- · Enterprises with strong MLOps practices
- · Responsible AI consultants
- · ML engineers focusing on deployment
- · Companies with poor AI governance
- · AI ventures neglecting production robustness
- · Legacy quality assurance approaches
- · Organizations relying solely on traditional metrics
Enterprises will increasingly invest in specialized tools and teams for evaluating and monitoring AI systems in production.
A new industry standard for 'AI evaluation stacks' and 'diagnostic maturity models' will emerge, similar to DevOps and MLOps.
Regulatory frameworks may begin to mandate specific evaluation and audit practices for AI systems deemed critical or high-risk.
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Read at InfoQ