Observe and troubleshoot apps on Azure
At a glance
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This learning path teaches you how to gain end-to-end observability into distributed AI applications on Azure. You start by instrumenting applications with OpenTelemetry to capture distributed traces, create custom spans, and export telemetry to Azure Monitor Application Insights. You then analyze the collected telemetry by writing KQL queries, exploring error patterns and performance trends, building dashboards and workbooks for operational visibility, and configuring alerts to detect failures and anomalies.
Prerequisites
- Programming experience with Python.
- Basic understanding of Azure services and cloud computing concepts.
- Familiarity with distributed application architectures.
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Modules in this learning path
Learn how to instrument distributed applications with OpenTelemetry on Azure, create custom spans and traces, export telemetry to Azure Monitor Application Insights, and use trace data to debug performance issues in distributed AI solutions.
Learn to write KQL queries against Application Insights logs, explore error patterns and performance trends, build dashboards and workbooks for ongoing visibility, and configure alerts to detect failures and anomalies in AI solutions on Azure.