Build high-performant AI solutions
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.
What you'll learn from this whitepaper:
- 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."