GPU capacity depends on power and cooling
When inference slows without obvious application errors, test physical constraints alongside queueing, workload changes, and software regressions.
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When inference slows without obvious application errors, test physical constraints alongside queueing, workload changes, and software regressions.
Test evidence retrieval, long-session consistency, tool boundaries, and cost before letting a model influence production changes.
Connect on-call interruptions, specialist demand, and recovery work to a concrete capacity decision using an on-call workload worksheet.
Turn reliability policy into a release decision with fresh evidence, limited exceptions, and a clear path to resuming normal changes.
Read validated architectures as a starting point for workload, recovery, and support testing, not a promise of automatic reliability.
Compare detection, correlation, investigation, and automation using your incidents, review costs, and failure paths.
Understand where detection, correlation, forecasting, and language models can help, and the operational work each introduces.
Understand the encoder, latent distribution, training objective, and why reconstruction error is evidence to evaluate rather than a diagnosis.
Understand how GANs train and why synthetic data needs checks for coverage, constraints, privacy, and downstream usefulness.
Connect an AIOps investment to a measurable workflow, its full operating cost, and the evidence needed to expand or stop the trial.
Build a ticket-routing evaluation around real ownership, uncertain cases, and correction cost. Start with an honest baseline before adding a model.