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Unpacking Two Emerging AI Observability Approaches for Monitoring MLOps and LLMOps | Fiddler AI Blog

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With AI adoption surging, forward-thinking enterprises are deploying an increasing number of AI-powered business applications every day. As these algorithms become increasingly intricate, the need for comprehensive model monitoring platforms has become clear. Enterprises have recognized this need, which helps AI Observability vendors further advance best practices to monitor these business critical systems. As the dust settles, it is clear that two paradigms for AI Observability have emerged — one that focuses on publishing metrics for observation and one that focuses on publishing inferences for observation. In this blog, we will explore the advantages and disadvantages inherent to these two approaches, and analyze their trade-offs. Monitoring key model metrics over time is a cornerstone of any proper AI Observability platform. Metrics around data quality, data drift, and model performance are essential to measure on an ongoing basis to ensure high performing models. Any AI Observabil

Metrics vs. Inferences in AI Observability Explained | Fiddler AI Blog Blog / Model Monitoring / Choosing Between Metrics and Inferences for Model Monitoring Published on February 15, 2024 Last Edited July 15, 2025 Choosing Between Metrics and Inferences for Model Monitoring Unpacking two emerging approaches for AI Observability Danny Brock VP of Customer Success Karen He Principal Product Marketing Manager Table of Contents Model Monitoring MLOps Generative AI and LLMOps With AI adoption surging, forward-thinking enterprises are deploying an increasing number of AI-powered business applicatio

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