Deploying a model in production can be very tricky so we developed a lot of features to help to deploy:
- In real-time, with API deployer you can deploy this model on a static API or on a Kubernetes cluster.
- In batch, with Automation node where the model can be scheduled to score new records every day.
In both modes, we have features to monitor the models, follow the metrics, update it and manage the versions.
It's also possible to export the model in several formats, if you want to do something else.
You can check this page of the doc:
Isn't the following possible?
from keras.models import load_model
model.save('my_model.h5') # creates a HDF5 file 'my_model.h5'