Capture and view telemetry

Application Insights automatically captures any information written to the standard output and error logs, and provides a query capability to view data in these logs.

Write log data

To capture telemetry data for Application insights, you can write any values to the standard output log in the scoring script for your service by using a print statement, as shown in the following example:

def init():
    global model
    model = joblib.load(Model.get_model_path('my_model'))
def run(raw_data):
    data = json.loads(raw_data)['data']
    predictions = model.predict(data)
    log_txt = 'Data:' + str(data) + ' - Predictions:' + str(predictions)
    print(log_txt)
    return predictions.tolist()

Azure Machine Learning creates a custom dimension in the Application Insights data model for the output you write.

Query logs in Application Insights

To analyze captured log data, you can use the Log Analytics query interface for Application Insights in the Azure portal. This interface supports a SQL-like query syntax that you can use to extract fields from logged data, including custom dimensions created by your Azure Machine Learning service.

For example, the following query returns the timestamp and customDimensions.Content fields from log traces that have a message field value of STDOUT (indicating the data is in the standard output log) and a customDimensions.["Service Name"] field value of my-svc.

traces
|where message == "STDOUT"
  and customDimensions.["Service Name"] = "my-svc"
| project  timestamp, customDimensions.Content

This query returns the logged data as a table: