rollback

A classic build screen: a key-value store where writes inside a transaction can be committed or thrown away, and transactions nest. The trap is mutating the base store directly. Here is the overlay-stack design that makes rollback O(1).

Deploying a model is not flipping a switch. The signal is knowing what each rollout strategy validates, why shadow is uniquely useful for ML, and keeping rollback one step away. Here is the comparison and when to use each.

A bad model in production needs a fast, safe rollback, but ML rollback is trickier than code: the model is data and the truth signal lags. The signal is pre-defined criteria plus a previous version kept warm. Here is the answer.