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Enrich a search index using an Azure Machine Learning model

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You create your Azure Machine Learning model using developer tools like the Python SDK, REST APIs, or Azure CLI. Another option is to take advantage of the Azure AI Machine Learning studio, a graphical user interface that lets you create, train, and deploy models without writing any code.

With a model created, you alter how the scoring code calls the model to allow it to be used by your custom search skill.

The last steps are to create a Kubernetes cluster to host an endpoint for your model.

Create an AML workspace

When you create the AML workspace, Azure will also create storage accounts, a key store, and application insights resources. The AML workspace Overview pane gives you a link to launch the Azure AI Machine Learning Studio.

Create and train a model in Azure Machine Learning studio

Azure AI Machine Learning Studio lets you use a designer to use drag and drop to create pipelines that create and train models. There's an even easier way to create models by using prebuilt templates.

However you choose to create your models, they need to be registered in Azure AI Machine Learning Studio so that you can deploy the model to a web service.

Alter how the model works to allow it to be called by the AML custom skill

The models you train will normally use many examples of the data. The datasets will have many rows and be split and used to train and test the model. The code that handles this data and passes it to the model needs to be changed to handle single rows.

The JSON response from the model should also contain only the output prediction.

For example, if your data is an array of JSON objects:

[ 
    {
        "attribute-1": null,
        "attribute-2": null
    },
    {
        "attribute-1": null,
        "attribute-2": null
    },
    {
        "attribute-1": null,
        "attribute-2": null
    }
]

The python scoring code will have to process the data a row at a time:

data = json.loads(data)
for row in data:
    for key, val in row.items():
        input_entry[key].append(decode_nan(val))

To change the input dataset to a single record:

{
    "attribute-1": null,
    "attribute-2": null
}

The python code will need to change to:

data = json.loads(data)
for key, val in data.items():
    input_entry[key].append(decode_nan(val))

For the response from the scoring code, the default code returns the whole JSON document:

return json.dumps({"result": result.data_frame.values.tolist()})

The custom skill needs to be able to map a single response from the model. So the code should return JSON that is only the last attribute.

output = result.data_frame.values.tolist()
# return the last column of the the first row of the dataframe
return {
    "predicted_outcome": output[0][-1]
}

Create an endpoint for your model to use

The model is deployed to an endpoint. Azure AI Machine Learning Studio supports deploying a model to a real-time endpoint, a batch endpoint, or a web service. At the moment, the custom AmlSkill skill in Azure AI Search only supports web service endpoints.

The other restriction is that the endpoint has to be an Azure Kubernetes Service (AKS), container instances aren't supported.

If you have experience in creating and managing AKS clusters, you can manually create the clusters in the Azure portal and reference them when you create your endpoint. However, an easier option is to let Azure AI Machine Learning Studio create and manage the cluster for you.

If you navigate to the compute section of the studio, you can create inference clusters. AML studio will then guide you through choosing the size of the cluster and even enable HTTPS and create a domain name for you. It will be in the format of location.cloudapp.azure.com:443.

Connect the AML custom skill to the endpoint

With everything above in place, you need to update your Azure AI Search service. First, to enrich your search index you'll add a new field to your index to include the output for the model.

Then you'll update your index skillset and add the #Microsoft.Skills.Custom.AmlSkill custom skill.

Next, you'll change your indexer to map the output from the custom skill to the field you created on the index.

The last step is to rerun your indexer to enrich your index with the AML model.

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