mlops

The code did not change, the data did. That inverts what triggers the pipeline, what the tests assert, and what a rollback means.

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

The question that separates people running GPU clusters from people running Kubernetes clusters that happen to have GPUs. The answer is that an integer count cannot express what a GPU workload needs.

The fastest-growing design round in infrastructure hiring. GPU economics break the assumptions CPU autoscaling is built on, and the answer has to start from that rather than from Kubernetes.

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.

A training job is an hours-long computation with no user watching. Whether an interruption costs four minutes or four days is decided entirely by checkpoint cadence and restart semantics designed before the run.

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.

A model is an artifact with a lineage, an evaluation record and an owner, or it is a file somebody copied to a bucket. The registry exists so that the second one cannot reach production.

Before blaming the model, check whether it is being fed the same thing it was trained on. Most of the gap is a pipeline difference, and pipeline differences are a platform problem with a platform fix.