Registering models and production artifacts
In this step, the model that has been serialized or containerized in the previous step is registered and stored in the model registry. A registered model is compiled as a logical container for one or more files that function as a model. For instance, a model made up of multiple files can be registered as a single model in the model registry. By downloading the registered model, all the files can be received. The registered model can be deployed and used for inference on demand.
Let's register our serialized models in the previous section by using the model .register() function from the Azure ML SDK. By using this function, the serialized ONNX file is registered to the workspace for further use and deploying to the test and production environment. Let's register the serialized SVM classifier model (svc.onnx):
# Register Model on AzureML WS model = Model.register (model_path = './outputs/svc.onnx', # this points to...
