Using ChatGPT at work: check the account, data, and destination
Make a useful AI request without assuming that tool approval, anonymization, or a training opt-out settles every data-handling question.
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Make a useful AI request without assuming that tool approval, anonymization, or a training opt-out settles every data-handling question.
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.
Connect an AIOps investment to a measurable workflow, its full operating cost, and the evidence needed to expand or stop the trial.
Containers package processes; orchestration reconciles desired state. Reliability still depends on probes, capacity, dependencies, and application behavior.
Translate incident findings into funded changes with an owner, a test, and evidence that the relevant failure mode became less likely or less costly.
Protect time away through explicit coverage, recovery policies, and realistic planning instead of relying on individual boundary-setting alone.
Start tracing with a customer-critical path, then test propagation, sampling, and whether the trace supports a real investigation.
An error budget translates an SLO into an allowed amount of bad service. Its value comes from the decisions attached to consumption and burn rate.
Choose a small set of reliability indicators with clear definitions, useful distributions, and an explicit decision attached to each.
Use the free SRE books as references for a concrete service problem, then adapt and test the practices against your own constraints.