Understanding the various deployment options
We have been working with Python code since Chapter 8, Experimenting with Python Code. So far, you have trained various models, evaluated them based on metrics, and saved the trained model using the dump method of the joblib library. The AzureML workspace allows you to store and version those artifacts by registering them in the model registry that we discussed in Chapter 5, Letting the Machines Do the Model Training. Registering the model allows you to version both the saved model and the metadata regarding the specific model, such as its performance according to various metrics. You will learn how to register models from the SDK in the Registering models in the workspace section.
Once the model has been registered, you have to decide how you want to operationalize the model, either by deploying a real-time endpoint or by creating a batch process, as displayed in Figure 12.1:
Figure 12.1 – A path from training...
