Agnostic AI observability frameworks, monitoring patterns, OpenTelemetry for AI, LLM tracing, production ML monitoring
As AI systems increasingly operate in production environments, ensuring the reliability of model explanations becomes critical for trust and accountability. This article presents a framework for monitoring explainability drift—the degradation of explanation quality over time—in deployed machine learning models. We define explainability drift as a measurable divergence between expected and obser...
As AI-driven predictive maintenance (PdM) systems become integral to smart manufacturing operations, ensuring the quality and reliability of their explanations is critical for safety, compliance, and operational trust. This article extends the AI observability framework to manufacturing AI systems, focusing on explanation quality monitoring in predictive maintenance contexts. We define a specia...
Modern AI systems deployed in production remain fundamentally opaque to the engineers who operate them. While OpenTelemetry has emerged as the de facto standard for distributed systems observability, its extension to AI and large language model (LLM) workloads exposes critical gaps: latency traces do not capture hallucination rates, infrastructure metrics do not surface semantic drift, and no v...