We do cross-validation tests to see how well the model predicts actual
behavior. As to the best data mix, cross-validation works with any engine
tuning or data input. Typically this requires re-traiing between test runs so
make sure you use exatly the same training/test split. If you want to
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
Hi,
With the Universal Recommender,
1. How can we validate the model after we train and deploy it?
2. How can we find an appropriate method of data mixing ??
Thanks
--
Saarthak Chandra,
Masters in Computer Science,
Cornell University.
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
