Canary deployments: limit exposure and test the comparison
Choose representative traffic, define promotion criteria, and verify rollback compatibility before expanding a release.
Practical AI operations. Reliable systems.
Walkthroughs for observability, incident response, and AI-assisted operations.
Start with what AI can do for operations, and where judgment still matters.
Read AIOps fundamentals ↗Make metrics, logs, and traces work together.
Read Observability for SRE ↗Build an incident response practice that learns.
Read Incident management with AI ↗Instructions, examples, and implementation notes.
Choose representative traffic, define promotion criteria, and verify rollback compatibility before expanding a release.
Use consistent onset and discovery timestamps, include customer-reported incidents, and interpret the average with its sample and uncertainty.
Counters, gauges, and distributions answer different questions. Build metrics that preserve scope, denominators, and the evidence behind an incident.
Design logs around useful events, searchable context, and a collection path whose failures are visible.
Define coverage, escalation, training, and recovery time before assigning the calendar. Review workload as well as shift counts.
Download a Markdown runbook template and follow a database connection example that separates symptoms, mitigation decisions, and verified recovery.
Containers package processes; orchestration reconciles desired state. Reliability still depends on probes, capacity, dependencies, and application behavior.
Understand spans, parent-child relationships, retries, and missing evidence so traces support a careful diagnosis.
Use the free SRE books as references for a concrete service problem, then adapt and test the practices against your own constraints.