AIOps
Evaluate AIOps tools with the work your responders actually do
Compare detection, correlation, investigation, and automation using your incidents, review costs, and failure paths.
SRE
Writing on SRE.
AIOps
Use AIOps to shorten the part of incident response that is slow
Locate the delay, choose a bounded intervention, and measure recovery quality alongside elapsed time.
How-To
Customer reliability engineering: connect the report to the service
Translate customer symptoms into an owned investigation while preserving impact, uncertainty, and a useful communication loop.
AIOps
Where ChatGPT can help an SRE, and what to verify
Use language-model assistance for evidence review, draft communication, and practice, with a clear boundary around production decisions.
How-To
Alert fatigue in SRE: reduce unnecessary pages without losing coverage
Review urgent-action criteria, duplicate notifications, and missed failures before adding model-based suppression.
AIOps
AI in release engineering: improve evidence without weakening the gate
Use models for test suggestions and change analysis while keeping artifact identity, promotion rules, and rollback checks enforceable.
AIOps
What AIOps changes in the SRE workflow
Understand where detection, correlation, forecasting, and language models can help, and the operational work each introduces.
Leadership & Culture
Incident management in SRE: coordinate recovery and verify the result
A practical incident lifecycle covering declaration, roles, mitigation, customer updates, and the handoff into learning.
Leadership & Culture
SRE accountability needs authority, capacity, and a clear owner
Define who does the work, who decides, and who accepts risk so ownership remains useful during incidents and follow-up.
How-To
Canary deployments: limit exposure and test the comparison
Choose representative traffic, define promotion criteria, and verify rollback compatibility before expanding a release.
AIOps
Generative adversarial networks: realism is not operational validity
Understand how GANs train and why synthetic data needs checks for coverage, constraints, privacy, and downstream usefulness.
How-To
Mean time to detect: calculate MTTD and expose measurement gaps
Use consistent onset and discovery timestamps, include customer-reported incidents, and interpret the average with its sample and uncertainty.