Operations | Monitoring | ITSM | DevOps | Cloud

AI applications can behave unpredictably, potentially leading to errors such as hallucinations or data leaks, even when classic monitoring indicates a successful response. To effectively monitor AI systems, four key areas should be focused on. Implementing these pillars can enhance trust in AI deployments, help manage costs, and identify safety issues before they impact users.

A semantic-convention rename (http.method → http.request.method) can silently break your RED metrics — no errors, just gaps in dashboards and alerts. The OpenTelemetry Collector's schema processor fixes it: put it first in your pipeline and it normalizes attribute names no matter what each service emits. Migration mode writes BOTH the old and new names, so you get zero-downtime upgrades while queries keep working.

This walk-through shows how Splunk's Cloud can discover schema and partition keys for Microsoft Azure Blob Storage datasets and create searchable Splunk managed tables. Once the data is mapped, analysts can use Splunk Federated Search to query Azure Blob data where it lives, bringing cloud-resident logs into security, observability, and operational work-flows without re-ingesting the data.

This walk-through shows how Splunk's Cloud can discover schema and partition keys for Microsoft Azure Blob Storage datasets and create searchable Splunk managed tables. Once the data is mapped, analysts can use Splunk Federated Search to query Azure Blob data where it lives, bringing cloud-resident logs into security, observability, and operational work-flows without re-ingesting the data.