AI Platforms & Models

Model Management

Once a model change is capable of altering behaviour people rely on, which is immediately after the first production deployment.

Model management makes changing a model a controlled operation rather than an incident. That requires versioning, evaluation against a fixed suite, and a rollout path that can be reversed.

The evaluation suite is the part organizations skip and later wish they had, because without it there is no way to distinguish a better model from a differently-wrong one.

What the work includes

  • Versioning of models and their configuration together
  • Evaluation suites run before promotion between environments
  • Staged rollout with the ability to roll back quickly
  • Records of which version produced which output, for later review
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Common questions about Model Management.

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Because a model change can alter behaviour people depend on without any code changing. A prompt tuned for one version can degrade on the next, and without version pinning that regression arrives unannounced, in production, attributable to nothing.

By running it against a held-out set of real requests with known-good outputs and comparing. This requires having captured that baseline beforehand, which is the part usually skipped and the part that makes the comparison possible.

Yes, for anything people depend on. Automatic upgrades trade a known behaviour for an unknown one at a time you did not choose. Pinning makes the upgrade a decision with a test behind it rather than an event you discover from a complaint.

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