removed notebook reference

Author: liakaz

articles/machine-learning/how-to-deploy-and-where.md

@@ -542,7 +542,7 @@ input_json = {'data': [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                        [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]]}
 # create a string that can be utf-8 encoded and
 # put in the body of the request
-serialized_input_json = input_json
+serialized_input_json = json.dumps(input_json)
 dataset_content = []
 for i in range(100):
     dataset_content.append(serialized_input_json)
@@ -577,7 +577,7 @@ inference_config = InferenceConfig(entry_script='path-to-score.py',
                                    environment=myenv)
 input_dataset = Dataset.get_by_name(workspace=ws, name='sample_request_data')
 profile = Model.profile(ws,
-            'unique_name_of_profiling_job',
+            'unique_name',
             [model],
             inference_config,
             input_dataset=input_dataset)
@@ -594,8 +594,6 @@ The following command demonstrates how to profile a model by using the CLI:
 az ml model profile -g <resource-group-name> -w <workspace-name> --inference-config-file <path-to-inf-config.json> -m <model-id> --idi <input-dataset-id> -n <unique-name>
 ```
 
-For an end to end example which shows how to use Azure Machine Learning Profiling, please see [this notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/deployment/deploy-to-cloud)
-
 ## Deploy to target
 
 Deployment uses the inference configuration deployment configuration to deploy the models. The deployment process is similar regardless of the compute target. Deploying to AKS is slightly different because you must provide a reference to the AKS cluster.