Meanwhile, the next (might be multiple) release candidates are being developed/trained an tested for potential future production use.
e.g. When I did autonomous robotics, the sensor models had to be quite adaptive as less predictable environmental parameters such as lightning conditions, dirt, energy level and temperature could influence readings dramatically. These dynamic adaptations occur at runtime, sometimes by a fairly non trivial trained sensor model.
What you usually do not want is running an untested system that "freely" learns from presented data in a live production environment as that could lead e.g. to contextual over-fitting or destabilization and even subversion of the adaptive control processes.
Exceptions could be systems that have to operate in extremely dynamic and less understood environments, but where risks are bound and you can confidently implement guardrails to protect against excessive loss (e.g. HFT agents).