Communications of the ACM
AI and ML Observability
Credit: Software/Andy Dang
Andy Dang is head of Engineering at WhyLabs.
In an interview, Andy Dang discusses observability and data ops for artificial intelligence (AI)/machine learning (ML) applications and how that differs from traditional observability. During the podcast, Dang discusses running an AI/ML model in production and how observability is an important tool in diagnosing and detecting various failures in the application.
The interview explores concept drift and data drift as indicators in assessing a model's quality and what corrective actions to take. Dang describes the challenges arising from high dimensionality and data volume, as well as from organizational structures that manage and operate various aspects of the data infrastructure and how observability can detect and solve problems in production.
From Software Engineering Radio
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