In the preceding example, we explained how to deploy an ML application using Docker to encompass it and its dependencies. We deliberately stayed away from any discussion pertaining to the infrastructure that was going to run these containers or any Platform-as-a-Service offerings that could facilitate the development or deployment itself. In the current section, we consider different deployment models for ML applications under the assumption that the application will be deployed to a cloud platform that supports both IAAS and platform-as-a-service models, such as Microsoft Azure and Amazon Web Services.
This section is specifically written to help you decide what virtual infrastructure to use if you are deploying an ML application to the cloud.
There are two main deployment models for any cloud application:
- Infrastructure-as-a-service:...
