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staged deployment

nounAlso: staged release, staggered release, gradual release, phased rollout, incremental deployment

In plain wordsReleasing a new AI system step by step — first to testers, then to limited groups, then widely — so that problems can be spotted and fixed before everyone has it.

  1. A release strategy in which an AI system is made available in successive stages of widening access — internal use, trusted testers or researchers, limited or rate-limited APImodel API, n.: A web service through which programs send requests to an AI model run by a company and receive its answers, usually paying per use instead of running the model themselves. access, then general availability — with monitoring and evaluation between stages so that risks can be detected and mitigated, or the rollout halted, before full exposure. Stages may also widen capability rather than audience, for example by releasing progressively larger versions of a model or enabling fine-tuning, tool use or weight downloads only after review. Staged deployment trades speed for information about real-world use that pre-deployment testingpre-deployment testing, n.: Checking a new AI model for dangerous abilities and bad behavior before it is released to the public, sometimes with outside experts or government testers given early access. cannot provide. It is weakened by competitive pressure and does not apply to open weightsopen-weight model, n.: An AI model whose trained internal numbers are published for anyone to download and run on their own computers, even if the data and code used to make it stay private., which cannot be recalled once published.

    Under staged deployment, the voice feature reached a thousand trusted testers a month before general release.

Origin

Adapts the gradual rollouts long used in software engineering. OpenAI's staged release of GPT-2 in 2019 — four model sizes published between February and November, accompanied by a report by Irene Solaiman and colleagues — made the practice widely discussed. Solaiman's 'The Gradient of Generative AI Release' (2023) mapped the options from fully closed to fully open.

Usage note

Overlaps with structured accessstructured access, n.: Letting people use an AI system through a controlled channel, such as a website or programming interface, instead of handing over the model itself, so that the developer can limit misuse., which concerns how access is given rather than when. In software engineering, staged (canary) rollouts aim mainly at reliability; in AI safety they also aim at learning about misuse and unforeseen capabilities.

Related

See also
  • structured accessstructured access, n.: Letting people use an AI system through a controlled channel, such as a website or programming interface, instead of handing over the model itself, so that the developer can limit misuse.
  • pre-deployment testingpre-deployment testing, n.: Checking a new AI model for dangerous abilities and bad behavior before it is released to the public, sometimes with outside experts or government testers given early access.
  • open-weight modelopen-weight model, n.: An AI model whose trained internal numbers are published for anyone to download and run on their own computers, even if the data and code used to make it stay private.
  • shadow deploymentshadow deployment, n.: Quietly running a new AI model alongside the old one on real requests, recording its answers without showing them to anyone, to check it behaves well before it takes over.
  • A/B testingA/B testing, n.: Comparing two versions of something — two models, two prompts, two designs — by randomly showing each to different groups of real users and measuring which works better.
  • system cardsystem card, n.: A public report released with an AI model that explains what it can do, how it was tested for safety, what risks were found and what protections were put in place.
Contrast with
  • shadow deploymentshadow deployment, n.: Quietly running a new AI model alongside the old one on real requests, recording its answers without showing them to anyone, to check it behaves well before it takes over.

Sources

  1. Solaiman, I. et al. (2019). Release Strategies and the Social Impacts of Language Models. OpenAI report, arXiv preprint.
  2. Solaiman, I. (2023). The Gradient of Generative AI Release: Methods and Considerations. FAccT 2023.

Mentioned in

pre-deployment testingpre-deployment testing, n.: Checking a new AI model for dangerous abilities and bad behavior before it is released to the public, sometimes with outside experts or government testers given early access., Project GlasswingProject Glasswing, prop. n.: An Anthropic program, launched in 2026, that gave a hacking-capable AI model only to organizations defending important software instead of releasing it to everyone., structured accessstructured access, n.: Letting people use an AI system through a controlled channel, such as a website or programming interface, instead of handing over the model itself, so that the developer can limit misuse.