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 not use 
Elasticsearch for their model storage. However I believe that some could employ 
the same hot-swap method to re-deploy, they just weren’t written that way. 
You’d have to say which Template you are using.


On Sep 6, 2017, at 12:19 AM, Paul-Armand Verhaegen 
<paularmand.verhae...@gmail.com> wrote:

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 that we 
can do both if business requires it. 

When working with UR: Another solution would be to utilise ES aliases

I'm pretty sure other people have thought of other solutions, but it mostly 
depends on the exact use case.
I hope that helps.

Paul

> On 6 Sep 2017, at 03:34, Saarthak Chandra <chandra.saart...@gmail.com> wrote:
> 
> 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.

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