AWS CloudWatch Metrics and Alarms: Practical Observability
Build a meaningful CloudWatch setup with custom metrics, composite alarms, and dashboards that catch real incidents without paging on noise.
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Build a meaningful CloudWatch setup with custom metrics, composite alarms, and dashboards that catch real incidents without paging on noise.
A tour of the modern observability stack: metrics, logs, traces, and events. Learn how the pillars fit together and how to choose tooling without drowning in dashboards.
Understand how HPA decides when to add or remove pods, the metrics it can scale on, and the tuning knobs that prevent flapping and runaway scaling.
A thorough look at the confusion matrix: how to read it, the metrics it produces, and how to use it to diagnose classifier behavior beyond a single accuracy number that often hides what is going wrong.
Decode precision, recall, F1, and accuracy with concrete intuition, threshold tuning, and PR vs ROC curve guidance for imbalanced data.
Measure RAG quality with recall@k, MRR, context precision, faithfulness, and answer relevancy so you can iterate on data, not vibes.
Line, branch, and mutation coverage explained. Learn what each metric tells you, what it hides, and how to use coverage without gaming it.