Configure diagnostic logging

Microsoft Azure provides various services for AI solutions, and it is crucial to configure diagnostic logging to ensure optimal performance and troubleshoot any issues that may arise. In this article, we will discuss how to configure diagnostic logging for Azure AI solutions.

Overview

Diagnostic logging allows you to capture and analyze information that can help you understand how your AI solution is performing and identify any errors or performance bottlenecks. It provides insights into the internal workings of your solution and can be a valuable tool for debugging and optimization.

Steps to Configure Diagnostic Logging

To configure diagnostic logging for your Azure AI solution, you can leverage Azure Monitor, which is a central tool used to gain visibility into the performance and health of Azure resources. Azure Monitor integrates with various Azure services, including AI services, and provides a unified view of your solution’s logs and metrics.

1. Create an Azure Monitor Workspace

Start by creating an Azure Monitor workspace. This workspace acts as a central repository for your solution’s logs and metrics. You can create a workspace through the Azure portal or using Azure PowerShell or Azure CLI.

2. Enable Diagnostic Settings

Once you have a workspace, you need to enable diagnostic settings for your AI service. Diagnostic settings allow you to specify what types of logs and metrics you want to collect.

1. Navigate to the Azure portal and open the Azure Monitor workspace you created.
2. Go to the “Diagnostic settings” blade and click on “Add diagnostic setting”.
3. Select the AI service for which you want to configure diagnostic logging.
4. Choose the logs and metrics you want to enable. For example, you can enable “RequestLogs” to capture information about incoming requests, or “ResponseLogs” to capture information about outgoing responses.
5. Specify the retention period for the logs and metrics. Azure Monitor allows you to retain log data for a specific period, after which it is automatically deleted.

3. View and Analyze Logs

Once diagnostic logging is enabled, the logs will start flowing into your Azure Monitor workspace. You can view and analyze these logs using various tools provided by Azure Monitor.

1. Open the Azure Monitor workspace and go to the “Logs” blade.
2. Here, you can run queries using the Kusto Query Language (KQL) to filter and analyze the logs. For example, you can run a query to identify any errors or exceptions that occurred during processing.

// Example KQL query to filter logs
AzureDiagnostics
| where ResourceType == "AIService"
| where Level == "Error"
| project Message

This query filters the logs for the AI service and selects only the logs with an error level. You can then project the desired information, such as the error message.

4. Set up Alerts

In addition to viewing logs, you can also set up alerts based on specific conditions or thresholds. Azure Monitor allows you to configure alert rules that can trigger based on the logs and metrics collected.

1. Open the Azure Monitor workspace and go to the “Alerts” blade.
2. Click on “New alert rule” to create a new alert.
3. Define the conditions for the alert, such as the severity level or a specific error message.
4. Specify the action to be taken when the alert is triggered, such as sending an email notification or invoking a web hook.

// Example KQL query for alert rule condition
AzureDiagnostics
| where ResourceType == "AIService"
| where Level == "Error"
| where Message contains "Out of memory"

This example query sets a condition for the alert to trigger if any error logs contain the message “Out of memory”.

By following these steps, you can configure diagnostic logging for your Azure AI solution and gain valuable insights into its performance and health. You can use the captured logs and metrics to troubleshoot any issues that may arise and optimize your solution for better efficiency.

Remember, configuring diagnostic logging is essential not only during the initial development and testing phase but also for ongoing monitoring and maintenance of your Azure AI solution.