Hi mates ! I'm in the beginning of the road of building a recommendation pipeline on top of Flink. I'm going to register a list of UDF python functions on job startups where each UDF is an ML model.
Over time new model versions appear in the ML registry and I would like to update my UDF functions on the fly without need to restart the whole job. Could you tell me, whether it's possible or not ? Maybe the community can give advice on how such tasks can be solved using Flink and what other approaches exist. Thanks a lot for your help and advice !
