Monitoring for success: how best to observe & explain AI

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.

Download the whitepaper to learn more about how to best explain and observe AI.

This paper focuses on the final hurdle to successful AI deployment and the last mile of MLOps - ML monitoring

This paper includes:

  • What is AI Observability and how it provides critical insights into the 'why' behind alerts
  • 5 operational challenges of monitoring AI and ML - model decay, data drift, data integrity, outliers, and bias
  • Fiddler's combined approach to AI Observability with monitoring and explainability

Take a peek inside:

"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."