evaluation

Teams need comparable evaluations before they approve a model change. Design versioned test data, recorded runs and release thresholds that a pipeline can enforce.

Code rollouts ask 'does it work'; model rollouts ask 'is it better', and the second question has no HTTP status code. Shadow traffic, offline eval gates and quality canaries replace the 500-rate dashboards you cannot use.

The infrastructure win is real and easy to measure. The quality regression is real, harder to measure, and will not show up in your latency dashboards at all.

Latency is flat, errors are zero, and the answers are worse. Quality regression is invisible to every metric a platform team normally owns, which is why it needs its own pipeline.

The training part is a job. Everything around it is the pipeline: data snapshots that are immutable, an evaluation gate with a threshold set in advance, and adapters versioned so a bad one is a routing change away from gone.

Explain an AI feature failure through its promised outcome, observed quality gap, bounded recovery and the evidence needed for another release.