Merge pull request #108686 from craigcaseyMSFT/vcraic0322

fix AM build issues

Author: rmca14

articles/machine-learning/how-to-consume-web-service.md

@@ -42,7 +42,7 @@ The [azureml.core.Webservice](https://docs.microsoft.com/python/api/azureml-core
 * `auth_enabled` - If key authentication is enabled, `True`; otherwise, `False`.
 * `token_auth_enabled` - If token authentication is enabled, `True`; otherwise, `False`.
 * `scoring_uri` - The REST API address.
-* `swagger_uri` - The address of the OpenAPI specification. This URI is available if you enabled automatic schema generation. For more information, see [Deploy models with Azure Machine Learning](how-to-deploy-and-where.md#schema).
+* `swagger_uri` - The address of the OpenAPI specification. This URI is available if you enabled automatic schema generation. For more information, see [Deploy models with Azure Machine Learning](how-to-deploy-and-where.md#optional-define-model-web-service-schema).
 
 There are a three ways to retrieve this information for deployed web services:
 
@@ -274,47 +274,47 @@ This example demonstrates how to use Go to call the web service created from the
 package main
 
 import (
-	"bytes"
-	"encoding/json"
-	"fmt"
-	"io/ioutil"
-	"net/http"
+    "bytes"
+    "encoding/json"
+    "fmt"
+    "io/ioutil"
+    "net/http"
 )
 
 // Features for this model are an array of decimal values
 type Features []float64
 
 // The web service input can accept multiple sets of values for scoring
 type InputData struct {
-	Data []Features `json:"data",omitempty`
+    Data []Features `json:"data",omitempty`
 }
 
 // Define some example data
 var exampleData = []Features{
-	[]float64{
-		0.0199132141783263, 
-		0.0506801187398187, 
-		0.104808689473925, 
-		0.0700725447072635, 
-		-0.0359677812752396, 
-		-0.0266789028311707, 
-		-0.0249926566315915, 
-		-0.00259226199818282, 
-		0.00371173823343597, 
-		0.0403433716478807,
-	},
-	[]float64{
-		-0.0127796318808497, 
-		-0.044641636506989, 
-		0.0606183944448076, 
-		0.0528581912385822, 
-		0.0479653430750293, 
-		0.0293746718291555, 
-		-0.0176293810234174, 
-		0.0343088588777263, 
-		0.0702112981933102, 
-		0.00720651632920303,
-	},
+    []float64{
+        0.0199132141783263, 
+        0.0506801187398187, 
+        0.104808689473925, 
+        0.0700725447072635, 
+        -0.0359677812752396, 
+        -0.0266789028311707, 
+        -0.0249926566315915, 
+        -0.00259226199818282, 
+        0.00371173823343597, 
+        0.0403433716478807,
+    },
+    []float64{
+        -0.0127796318808497, 
+        -0.044641636506989, 
+        0.0606183944448076, 
+        0.0528581912385822, 
+        0.0479653430750293, 
+        0.0293746718291555, 
+        -0.0176293810234174, 
+        0.0343088588777263, 
+        0.0702112981933102, 
+        0.00720651632920303,
+    },
 }
 
 // Set to the URI for your service
@@ -323,32 +323,32 @@ var serviceUri string = "<your web service URI>"
 var authKey string = "<your key or token>"
 
 func main() {
-	// Create the input data from example data
-	jsonData := InputData{
-		Data: exampleData,
-	}
-	// Create JSON from it and create the body for the HTTP request
-	jsonValue, _ := json.Marshal(jsonData)
-	body := bytes.NewBuffer(jsonValue)
-
-	// Create the HTTP request
-	client := &http.Client{}
-	request, err := http.NewRequest("POST", serviceUri, body)
-	request.Header.Add("Content-Type", "application/json")
-
-	// These next two are only needed if using an authentication key
-	bearer := fmt.Sprintf("Bearer %v", authKey)
-	request.Header.Add("Authorization", bearer)
-
-	// Send the request to the web service
-	resp, err := client.Do(request)
-	if err != nil {
-		fmt.Println("Failure: ", err)
-	}
-
-	// Display the response received
-	respBody, _ := ioutil.ReadAll(resp.Body)
-	fmt.Println(string(respBody))
+    // Create the input data from example data
+    jsonData := InputData{
+        Data: exampleData,
+    }
+    // Create JSON from it and create the body for the HTTP request
+    jsonValue, _ := json.Marshal(jsonData)
+    body := bytes.NewBuffer(jsonValue)
+
+    // Create the HTTP request
+    client := &http.Client{}
+    request, err := http.NewRequest("POST", serviceUri, body)
+    request.Header.Add("Content-Type", "application/json")
+
+    // These next two are only needed if using an authentication key
+    bearer := fmt.Sprintf("Bearer %v", authKey)
+    request.Header.Add("Authorization", bearer)
+
+    // Send the request to the web service
+    resp, err := client.Do(request)
+    if err != nil {
+        fmt.Println("Failure: ", err)
+    }
+
+    // Display the response received
+    respBody, _ := ioutil.ReadAll(resp.Body)
+    fmt.Println(string(respBody))
 }
 ```
 

articles/stream-analytics/machine-learning-udf.md

@@ -53,7 +53,7 @@ The following table describes each property of Azure ML Service functions in Str
 
 ## Supported input parameters
 
-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. You have to provide a sample input and output in your scoring script to [automatically generate a schema](../machine-learning/how-to-deploy-and-where.md#optional-automatic-schema-generation). The schema allows Stream Analytics to construct the JSON serialized request for any of the supported data types such as numpy, pandas and PySpark. Multiple input events can be batched together in a single request.
+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. You have to provide a sample input and output in your scoring script to [automatically generate a schema](../machine-learning/how-to-deploy-and-where.md#optional-define-model-web-service-schema). The schema allows Stream Analytics to construct the JSON serialized request for any of the supported data types such as numpy, pandas and PySpark. Multiple input events can be batched together in a single request.
 
 The following Stream Analytics query is an example of how to invoke an Azure Machine Learning UDF: