Monitor and collect data from ML web service endpoints

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In this article, you learn how to collect data from and monitor models deployed to web service endpoints in Azure Kubernetes Service (AKS) or Azure Container Instances (ACI) by querying logs and enabling Azure Application Insights via

In addition to collecting an endpoint's output data and response, you can monitor:

  • Request rates, response times, and failure rates
  • Dependency rates, response times, and failure rates
  • Exceptions

Learn more about Azure Application Insights.


Query logs for deployed models

To retrieve logs from a previously deployed web service, load the service and use the get_logs() function. The logs may contain detailed information about any errors that occurred during deployment.

from azureml.core.webservice import Webservice

# load existing web service
service = Webservice(name="service-name", workspace=ws)
logs = service.get_logs()

Web service metadata and response data


Azure Application Insights only logs payloads of up to 64kb. If this limit is reached then you may see errors such as out of memory, or no information may be logged.

To log information for a request to the web service, add print statements to your file. Each print statement results in one entry in the trace table in Application Insights, under the message STDOUT. The contents of the print statement will be contained under customDimensions and then Contents in the trace table. If you print a JSON string, it produces a hierarchical data structure in the trace output under Contents.

You can query Azure Application Insights directly to access this data, or set up a continuous export to a storage account for longer retention or further processing. Model data can then be used in the Azure Machine Learning to set up labeling, retraining, explainability, data analysis, or other use.

Use Python SDK to configure

Update a deployed service

  1. Identify the service in your workspace. The value for ws is the name of your workspace

    from azureml.core.webservice import Webservice
    aks_service= Webservice(ws, "my-service-name")
  2. Update your service and enable Azure Application Insights


Log custom traces in your service

If you want to log custom traces, follow the standard deployment process for AKS or ACI in the How to deploy and where document. Then use the following steps:

  1. To send data to Application Insights during inference, update the scoring file by adding print statements. To log more complex information, such as the request data and the response, us a JSON structure. The following example file logs the time the model is initialized, the input and output during inference, and the time any errors occur:


    Azure Application Insights only logs payloads of up to 64kb. If this limit is reached, you may see errors such as out of memory, or no information may be logged. If the data you want to log is larger 64kb, you should instead store it to blob storage using the information in Collect Data for models in production.

    import pickle
    import json
    import numpy 
    from sklearn.externals import joblib
    from sklearn.linear_model import Ridge
    from azureml.core.model import Model
    import time
    def init():
        global model
        #Print statement for appinsights custom traces:
        print ("model initialized" + time.strftime("%H:%M:%S"))
        # note here "sklearn_regression_model.pkl" is the name of the model registered under the workspace
        # this call should return the path to the model.pkl file on the local disk.
        model_path = Model.get_model_path(model_name = 'sklearn_regression_model.pkl')
        # deserialize the model file back into a sklearn model
        model = joblib.load(model_path)
    # note you can pass in multiple rows for scoring
    def run(raw_data):
            data = json.loads(raw_data)['data']
            data = numpy.array(data)
            result = model.predict(data)
            # Log the input and output data to appinsights:
            info = {
                "input": raw_data,
                "output": result.tolist()
            # you can return any datatype as long as it is JSON-serializable
            return result.tolist()
        except Exception as e:
            error = str(e)
            print (error + time.strftime("%H:%M:%S"))
            return error
  2. Update the service configuration

    config = Webservice.deploy_configuration(enable_app_insights=True)
  3. Build an image and deploy it on AKS or ACI.

For more information on logging and data collection, see Enable logging in Azure Machine Learning and Collect data from models in production.

Disable tracking in Python

To disable Azure Application Insights, use the following code:

## replace <service_name> with the name of the web service

Use Azure Machine Learning studio to configure

You can also enable Azure Application Insights from Azure Machine Learning studio when you're ready to deploy your model with these steps.

  1. Sign in to your workspace at

  2. Go to Models and select which model you want to deploy

  3. Select +Deploy

  4. Populate the Deploy model form

  5. Expand the Advanced menu

    Deploy form

  6. Select Enable Application Insights diagnostics and data collection

    Enable App Insights

View metrics and logs

Your service's data is stored in your Azure Application Insights account, within the same resource group as Azure Machine Learning. To view it:

  1. Go to your Azure Machine Learning workspace in the studio.

  2. Select Endpoints.

  3. Select your deployed service.

  4. Scroll down to find the Application Insights url and select the link.

    Locate Application Insights url

  5. In Application Insights, from the Overview tab or the Monitoring section in the list on the left, select Logs.

    Overview tab of monitoring

  6. To view information logged from the file, look at the traces table. The following query searches for logs where the input value was logged:

    | where customDimensions contains "input"
    | limit 10

    trace data

To learn more about how to use Azure Application Insights, see What is Application Insights?.

Export data for further processing and longer retention


Azure Application Insights only supports exports to blob storage. Additional limits of this export capability are listed in Export telemetry from App Insights.

You can use Azure Application Insights' continuous export to send messages to a supported storage account, where a longer retention can be set. The data is stored in JSON format and can be easily parsed to extract model data.

Azure Data Factory, Azure ML Pipelines, or other data processing tools can be used to transform the data as needed. When you have transformed the data, you can then register it with the Azure Machine Learning workspace as a dataset. To do so, see How to create and register datasets.

Continuous export

Example notebook

The enable-app-insights-in-production-service.ipynb notebook demonstrates concepts in this article.

Learn how to run notebooks by following the article Use Jupyter notebooks to explore this service.

Next steps