I am sharing here some highly relevant content published by Adriano Tanaka, a colleague at Oracle Brazil, on how to monitor AIDP Workbench using OCI Metrics, OCI Logging, and Grafana.
What stands out most is the practical approach: moving beyond a purely technical view of job execution to an operational dashboard featuring CPU and memory metrics, execution statuses, audit logs, and direct links to workflows within AIDP Workbench.
For those working in Analytics, Data Engineering, AI, and data platform operations, this type of monitoring is essential for three reasons:
- It helps identify failures and bottlenecks more quickly.
- It improves the traceability of job executions.
- It creates a more solid foundation for operating AI pipelines and workloads at an enterprise scale.
It is also worth highlighting the recommendation to use instance principal authentication and least-privilege IAM policies, thereby avoiding reliance on long-lived personal credentials.
In short: observability isn't just about "viewing metrics." It’s about building the confidence to operate data and AI with security, governance, and scale.
An excellent contribution to the Oracle technical community.
I recommend reading this for anyone exploring the Oracle AI Data Platform, OCI, Grafana, Logging, Metrics, and operational best practices for Data & AI environments.
Blog content here:
