AIOps
Variational autoencoders: the model and its anomaly-detection limits
Understand the encoder, latent distribution, training objective, and why reconstruction error is evidence to evaluate rather than a diagnosis.
Articles
Original AIOpsSRE articles, newest first. Find current releases and industry coverage in News.
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.
SRE
How blameless incident reviews make responsibility clearer
Examine what people knew and could do, then assign improvements that address the conditions behind the incident.
SRE
Run a post-incident review that explains the difficult decisions
Prepare evidence, reconstruct the timeline, examine contributing conditions, and choose follow-up proportionate to the incident.
SRE
Apply KISS to the configurations your responders must understand
Use operational state, visible defaults, and reversible transitions to evaluate complexity without discarding necessary capabilities.
How-To
Metrics for observability: choose the question before the chart
Counters, gauges, and distributions answer different questions. Build metrics that preserve scope, denominators, and the evidence behind an incident.
AIOps
Python logging for operations: record failure and preserve the exit status
Use Python logging to capture useful context without swallowing errors, duplicating exceptions, or exposing sensitive data.
AIOps
Ethical leadership in AIOps starts with operational decisions
Make AI-assisted routing and remediation accountable through visible evidence, meaningful overrides, and review of who bears the errors.
AIOps
AIOps Automated Remediation: A Practical SRE Workflow
Connect AI-assisted diagnosis to bounded runbooks, explicit approval gates, and independent recovery checks. Start with a practical SRE rollout checklist.
AIOps
Collect and aggregate operational data with Python
Validate the source data, preserve useful denominators, and distinguish a failed collection from a real zero before aggregating metrics.