Merge pull request #179924 from Blackmist/11891786-verbatims

fixing verbatims

Author: ShannonLeavitt

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

@@ -153,11 +153,11 @@ For more information, see the documentation for the [Model class](/python/api/az
   + Register a model from an `azureml.train.automl.run.AutoMLRun` object:
 
     ```python
-        description = 'My AutoML Model'
-        model = run.register_model(description = description,
-                                   tags={'area': 'qna'})
+    description = 'My AutoML Model'
+    model = run.register_model(description = description,
+                                tags={'area': 'qna'})
 
-        print(run.model_id)
+    print(run.model_id)
     ```
 
     In this example, the `metric` and `iteration` parameters aren't specified, so the iteration with the best primary metric will be registered. The `model_id` value returned from the run is used instead of a model name.
@@ -170,8 +170,18 @@ For more information, see the documentation for the [Model class](/python/api/az
 
 ## Define a dummy entry script
 
-[!INCLUDE [write entry script](../../includes/machine-learning-dummy-entry-script.md)]
+The entry script receives data submitted to a deployed web service and passes it to the model. It then returns the model's response to the client. *The script is specific to your model*. The entry script must understand the data that the model expects and returns.
 
+The two things you need to accomplish in your entry script are:
+
+1. Loading your model (using a function called `init()`)
+1. Running your model on input data (using a function called `run()`)
+
+For your initial deployment, use a dummy entry script that prints the data it receives.
+
+:::code language="python" source="~/azureml-examples-main/python-sdk/tutorials/deploy-local/source_dir/echo_score.py":::
+
+Save this file as `echo_score.py` inside of a directory called `source_dir`. This dummy script returns the data you send to it, so it doesn't use the model. But it is useful for testing that the scoring script is running.
 
 ## Define an inference configuration
 
@@ -301,7 +311,7 @@ python_packages = ['nltk', 'numpy', 'onnxruntime']
 for package in python_packages:
     env.python.conda_dependencies.add_pip_package(package)
 
-inf_config = InferenceConfig(environment=env, source_directory='./source_dir', entry_script='./score.py')
+inference_config = InferenceConfig(environment=env, source_directory='./source_dir', entry_script='./score.py')
 ```
 
 For more information, see the documentation for [LocalWebservice](/python/api/azureml-core/azureml.core.webservice.local.localwebservice), [Model.deploy()](/python/api/azureml-core/azureml.core.model.model#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false-), and [Webservice](/python/api/azureml-core/azureml.core.webservice.webservice).