While monitoring by itself provides real-time issue visibility, it is often insufficient to identify the root cause of issues given the AI system’s complexity. Observability, a means to deduce internal state from its external outputs, is therefore critical to know the ‘why’ for a quick resolution. Explainable AI enables the deployment of high-risk AI solutions while AI Observability increases the success of these AI deployments.
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This paper focuses on the final hurdle to successful AI deployment and the last mile of MLOps - ML monitoring.
This paper includes:
"Operational Challenges in AI Today, there are two approaches to monitor production software:
● Service or infrastructure monitoring used by DevOps to get broad operational visibility and service health
● Business metrics monitoring via telemetry used by business owners to track business health.
Neither approach provides the critical ML model level insights that a Data Scientist or ML developer needs to operationalize a deployed model."