Merge pull request #42775 from sudivate/patch-2

Removed duplicate content

Author: ktoliver

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

@@ -1059,78 +1059,6 @@ To stop the container, use the following command from a different shell or comma
 docker kill mycontainer
 ```
 
-## (Preview) No-code model deployment
-
-No-code model deployment is currently in preview and supports the following machine learning frameworks:
-
-### Tensorflow SavedModel format
-
-```python
-from azureml.core import Model
-
-model = Model.register(workspace=ws,
-                       model_name='flowers',                        # Name of the registered model in your workspace.
-                       model_path='./flowers_model',                # Local Tensorflow SavedModel folder to upload and register as a model.
-                       model_framework=Model.Framework.TENSORFLOW,  # Framework used to create the model.
-                       model_framework_version='1.14.0',            # Version of Tensorflow used to create the model.
-                       description='Flowers model')
-
-service_name = 'tensorflow-flower-service'
-service = Model.deploy(ws, service_name, [model])
-```
-
-### ONNX models
-
-ONNX model registration and deployment is supported for any ONNX inference graph. Preprocess and postprocess steps are not currently supported.
-
-Here is an example of how to register and deploy an MNIST ONNX model:
-
-```python
-from azureml.core import Model
-
-model = Model.register(workspace=ws,
-                       model_name='mnist-sample',                  # Name of the registered model in your workspace.
-                       model_path='mnist-model.onnx',              # Local ONNX model to upload and register as a model.
-                       model_framework=Model.Framework.ONNX ,      # Framework used to create the model.
-                       model_framework_version='1.3',              # Version of ONNX used to create the model.
-                       description='Onnx MNIST model')
-
-service_name = 'onnx-mnist-service'
-service = Model.deploy(ws, service_name, [model])
-```
-
-### Scikit-learn models
-
-No code model deployment is supported for all built-in scikit-learn model types.
-
-Here is an example of how to register and deploy a sklearn model with no extra code:
-
-```python
-from azureml.core import Model
-from azureml.core.resource_configuration import ResourceConfiguration
-
-model = Model.register(workspace=ws,
-                       model_name='my-sklearn-model',                # Name of the registered model in your workspace.
-                       model_path='./sklearn_regression_model.pkl',  # Local file to upload and register as a model.
-                       model_framework=Model.Framework.SCIKITLEARN,  # Framework used to create the model.
-                       model_framework_version='0.19.1',             # Version of scikit-learn used to create the model.
-                       resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=0.5),
-                       description='Ridge regression model to predict diabetes progression.',
-                       tags={'area': 'diabetes', 'type': 'regression'})
-                       
-service_name = 'my-sklearn-service'
-service = Model.deploy(ws, service_name, [model])
-```
-
-NOTE: These dependencies are included in the prebuilt sklearn inference container:
-
-```yaml
-    - azureml-defaults
-    - inference-schema[numpy-support]
-    - scikit-learn
-    - numpy
-```
-
 ## Clean up resources
 
 To delete a deployed web service, use `service.delete()`.