The UR does this automatically. Once deployed you never have to deploy a second
time. When a new `pio train` happens the new model is hot-swapped to replace
the old, which is then erased, so there is no re-deploy and no downtime.
Yes, it uses Elasticsearch aliases but most other Templates do
I believe there are 2 main methods:
1. stop serving a couple of seconds while deploying the newly trained model,
this is supported from pio as is.
2. make a more flexible solution that can route traffic differently or cache
results. We have a reverse proxy (openresty / nginx + lua) in front, so
Hi,
Is there a way we can train a model without having to stop serving.
I mean, if I have an app deployed, can I add/post new data to the event
server and train the same app without stopping it?
Thanks!
--
Saarthak Chandra,
Masters in Computer Science,
Cornell University.
