Since it's possible to create custom components, one can for instance create a component for packaging models, for example the component can extract the model from a path (mounted volume or blob storage), create a python package with a requirements file, and a microservice based on flask, everytime a user wants to promote a model to production, she can run this component, or she can also add it to a workflow to be triggered after a training or hyperparams operation.
From Polyaxon's side, we will be providing a set of reusable components, some of these components will be targeting deployment and packaging, for instance aws lambda, azure ml, sagemaker, open source projects for deployment (some of them are mentioned in this HN thread) Users can also contribute components as well or create them to be used inside there organization.
For versioning, all component have versions, and all runs have full overview of their dependencies and provenance (inputs/outputs). This gives all information about when and how a model was created.
The platform knows how to create and manage services, e.g. tensorboards, notebooks, dash, simulators for RL agents ..., so one can also deploy the model as an internal Polyaxon service to be used as an internal tool or for testing purposes, the only thing to keep in mind is that the API endpoint will be subject to the same access rights as other components create by a given user.
