#AzureML – Creating a HTTP Rest Endpoint to consume an AzureML Designer based on the DenseNet template 🎨

Hi !

In my previous post I wrote about how to create a custom dataset with images to be used on a Azure Machine Learning Designer project. The second post was about how to use the custom data set and how to train the model. Today let’s publish the model to be used as a WebService / HTTP Rest endpoint.

Next step is to select the “real time inference pipeline” option from the [Create inference pipeline] option and press [Publish].

create inference pipeline

This will create a new designer pipeline project, related to the original one. A closer look at the new pipeline, shows new elements in the designer area

  • WebService Input
  • WebService Output

And also, on top, we can see how both designer pipelines are related.

new inference pipeline with webservice input and output

Let’s Submit this pipeline and wait for the completion of the process.

realtime inference pipeline run finished

Important: you can change the default names for WebServiceInput1 and WebServiceOutput1. This will help later with the JSON schema to consume the webservice.

Once the process is complete, we can deploy this running pipeline to be used as an HTTP REST Endpoint. I’ll create a new endpoint named [squirrelimageclassification], and a Compute Instance to host this resource.

setup realtime endpoint

A couple of minutes later, we are ready to go !

new endpoint created

When we open the new endpoint, we can review useful information like the HTTP endpoint !

endpoint details

And we even have a test section and sample code to test the Endpoint.

sample code to test the endpoint

Tomorrow I’ll show how to write a client desktop app to use the the generated endpoint!

Happy coding!

Greetings

El Bruno

More posts in my blog ElBruno.com.



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