Manage monitoring for AI-Ready Infrastructure: Review

Creating a metric alert in the Azure sandbox, then running a test script and seeing the alert actually fire—that was the lightbulb moment. Before that, 'log analytics workspace' and 'alert processing rules' were just abstract boxes on a slide deck my engineering team would show. I could finally visualize the path from a server hiccup to an engineer getting a notification.

Frankly, the 'AI-Ready' title is misleading. I was expecting something about monitoring GPU utilization for training runs or tracking inference latency. This is just standard Azure monitoring 101. It’s useful stuff—critical, even—but it has very little to do with the specific demands of AI workloads. The content is about setting up basic health checks for any cloud infrastructure. The Kusto Query Language (KQL) part was also a bit of a whirlwind. As a PM, I don't need to write the queries, but understanding *that* it's the language used to dig into logs is the key takeaway. Seeing the `| where TimeGenerated > ago(1h)` example was enough to get the concept.

This is for the product manager who sits in planning meetings and nods along when engineers talk about 'observability' but doesn't really know what it looks like. It won't make you an admin, but it will give you the confidence to ask better questions.