Integrate ML Model with IoT Hub

Add a machine learning model to stream analytics job

You can add Azure Machine Learning functions to your Stream Analytics job directly from the Azure portal

Azure portal

1. Navigate to your Stream Analytics job in the Azure portal, and select Functions under Job topology. Then, select Azure Machine Learning Service from the + Add dropdown menu.

2. Fill in the Azure Machine Learning Service function form.

3. 
4. Enter "Function Alias" and select "Provide Azure ML Service settings manually".

5. Get the value of the Scoring URL and Key from the Endpoint created in the ML workspace and enter in the Soring URI and Key field. This is available under Endpoints -> Consume in ML Workspace.

6. 
7. Once the Scoring URI and Key are entered, the function signature automatically loads. If your function signature fails to load, there could be a problem with the creation of the Endpoint in the ML workspace. Revisit the Endpoint creation process and ensure that Endpoint is working by using the Test option in Endpoints.

8. After entering all details press Save and after a while you should see a successful connection notification.

Calling machine learning endpoint from your query

When your Stream Analytics query invokes an Azure Machine Learning UDF, the job creates a JSON serialized request to the web service. The request is based on a model-specific schema that Stream Analytics infers from the endpoint's swagger.

The following Stream Analytics query is an example of how to invoke an Azure Machine Learning UDF:

SELECT udf.score(<model-specific-data-structure>)
INTO output
FROM input
WHERE <model-specific-data-structure> is not null

A more specific query is given below. This is an example from a weather prediction model where the machine learning service (function) takes three parameters i.e. isRain, temperature, humidity and returns predictions.

WITH Ip AS
(
SELECT 
    "isRain",
    "temperature",
    "humidity"
FROM
[iothubinput]
)
SELECT udf.weathermodel(Ip)
INTO
    [iothubbloboutput]
FROM
    Ip

If your input data sent to the ML UDF is inconsistent with the expected schema, the endpoint will return a response with error code 400, which will cause your Stream Analytics job to go to a failed state. Demo video from Microsoft.

Video Tutorial

Integrating ML Model with IoT Hub and Power BI

Query and Javascript UDF used in the video are given below.

Query

-- Select desired variables from the data that is being sent to the IoT Hub
-- Selected variables will get stored in the temporary storage called InputData
-- In this example two variables are being selected
WITH InputData AS (
    SELECT
        temperature,
        humidity
    FROM
        inputfromiothub002 -- Data is coming into this IoT Hub
),

-- Now take variables stored in InputData and run pass these variables to a function
-- udf.CreateJSONObjet function converts data into JSON format that the model Endpoint is expecting
-- Result will be stored as OutputData
OutputData AS (
    SELECT
        udf.CreateJSONObject(temperature, humidity) AS data -- Retuns JSON string as data variable
    FROM
        InputData
),

-- Select JSON data from OutputData and pass it on to the machine learning model endpoint
-- The model will return result that will contain multiple parameters
ModelOutput AS (
    SELECT
        udf.weathermodel(OutputData.data) AS result
    FROM
        OutputData
),

-- Extract desired parameters from the result of model store as FormattedOutput
FormattedOutput AS (
    SELECT
        System.Timestamp time,
        CAST(result.[temperature] AS FLOAT) AS temperature,
        CAST(result.[humidity] AS FLOAT) AS humidity,
        CAST(result.[Scored Probabilities] AS FLOAT) AS [probabilities of rain]
    FROM
        ModelOutput
)

-- Select everything from the formatted output and send it to iothubtopowerbi
SELECT
    *
INTO
    iothubtopowerbi
FROM
    FormattedOutput

-- Select everything from the formatted output and send it to iothuboutputstorage
-- Use this only if needed to store the data. Not needed for Power BI visualisation 
SELECT
    *
INTO
    iothuboutputstorage002
FROM
    FormattedOutput

Javascript UDF

// Sample UDF which returns sum of two values.
function CreateRecord(temperature, humidity) {
    return {
    " temperature": temperature,
    " humidity": humidity
  };
}