Merge pull request #214980 from pritamso/Broken-link-fix-dem108

Broken link fixed

Author: prmerger-automator[bot]

articles/machine-learning/how-to-access-resources-from-endpoints-managed-identities.md

@@ -96,7 +96,7 @@ This guide assumes you don't have a managed identity, a storage account or an on
     git clone https://github.com/Azure/azureml-examples --depth 1
     cd azureml-examples/sdk/endpoints/online/managed/managed-identities
     ```
-* To follow along with this notebook, access the companion [example notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/endpoints/online/managed/managed-identities/online-endpoints-managed-identity-sai.ipynb) within in the  `sdk/endpoints/online/managed/managed-identities` directory. 
+* To follow along with this notebook, access the companion [example notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/endpoints/online/managed/managed-identities/online-endpoints-managed-identity-uai.ipynb) within in the  `sdk/endpoints/online/managed/managed-identities` directory. 
 
 * Additional Python packages are required for this example: 
 
@@ -134,7 +134,7 @@ Install them with the following code:
     git clone https://github.com/Azure/azureml-examples --depth 1
     cd azureml-examples/sdk/endpoints/online/managed/managed-identities
     ```
-* To follow along with this notebook, access the companion [example notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/endpoints/online/managed/managed-identities/online-endpoints-managed-identity-uai.ipynb) within in the  `sdk/endpoints/online/managed/managed-identities` directory. 
+* To follow along with this notebook, access the companion [example notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/endpoints/online/managed/managed-identities/online-endpoints-managed-identity-uai.ipynb) within in the  `sdk/endpoints/online/managed/managed-identities` directory. 
 
 * Additional Python packages are required for this example: 
 

articles/machine-learning/how-to-deploy-custom-container.md

@@ -381,4 +381,4 @@ ml_client.online_endpoints.begin_delete(name=online_endpoint_name)
 
 - [Safe rollout for online endpoints](how-to-safely-rollout-managed-endpoints.md)
 - [Troubleshooting online endpoints deployment](./how-to-troubleshoot-online-endpoints.md)
-- [Torch serve sample](https://github.com/Azure/azureml-examples/blob/main/cli/deploy-torchserve.sh)
+- [Torch serve sample](https://github.com/Azure/azureml-examples/blob/main/cli/deploy-custom-container-torchserve-densenet.sh)

articles/machine-learning/how-to-deploy-managed-online-endpoints.md

@@ -286,7 +286,7 @@ For supported general-purpose and GPU instance types, see [Managed online endpoi
 
 ### Use more than one model
 
-Currently, you can specify only one model per deployment in the YAML. If you've more than one model, when you register the model, copy all the models as files or subdirectories into a folder that you use for registration. In your scoring script, use the environment variable `AZUREML_MODEL_DIR` to get the path to the model root folder. The underlying directory structure is retained. For an example of the scoring script for multi models, see [multimodel-minimal-score.py](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/custom-container/multimodel-minimal-score.py).
+Currently, you can specify only one model per deployment in the YAML. If you've more than one model, when you register the model, copy all the models as files or subdirectories into a folder that you use for registration. In your scoring script, use the environment variable `AZUREML_MODEL_DIR` to get the path to the model root folder. The underlying directory structure is retained. For an example of deploying multiple models to one deployment, see [Deploy multiple models to one deployment](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/custom-container/minimal/multimodel/README.md).
 
 ## Understand the scoring script
 
@@ -384,7 +384,7 @@ The output should appear similar to the following JSON. The `provisioning_state`
 ml_client.online_endpoints.get(name=local_endpoint_name, local=True)
 ```
 
-The method returns [`ManagedOnlineEndpoint` entity](/python/api/azure-ai-ml/azure.ai.ml.entities.managedonlineendpoint.md). The `provisioning_state` is `Succeeded`.
+The method returns [`ManagedOnlineEndpoint` entity](/python/api/azure-ai-ml/azure.ai.ml.entities.managedonlineendpoint). The `provisioning_state` is `Succeeded`.
 
 ```python
 ManagedOnlineEndpoint({'public_network_access': None, 'provisioning_state': 'Succeeded', 'scoring_uri': 'http://localhost:49158/score', 'swagger_uri': None, 'name': 'local-10061534497697', 'description': 'this is a sample local endpoint', 'tags': {}, 'properties': {}, 'id': None, 'Resource__source_path': None, 'base_path': '/path/to/your/working/directory', 'creation_context': None, 'serialize': <msrest.serialization.Serializer object at 0x7ffb781bccd0>, 'auth_mode': 'key', 'location': 'local', 'identity': None, 'traffic': {}, 'mirror_traffic': {}, 'kind': None})
@@ -587,7 +587,7 @@ for endpoint in ml_client.online_endpoints.list():
     print(endpoint.name)
 ```
 
-The method returns list (iterator) of `ManagedOnlineEndpoint` entities. You can get other information by specifying [parameters](/python/api/azure-ai-ml/azure.ai.ml.entities.managedonlineendpoint.md#parameters).
+The method returns list (iterator) of `ManagedOnlineEndpoint` entities. You can get other information by specifying [parameters](/python/api/azure-ai-ml/azure.ai.ml.entities.managedonlineendpoint#parameters).
 
 For example, output the list of endpoints like a table:
 

articles/machine-learning/how-to-deploy-with-triton.md

@@ -163,7 +163,7 @@ This section shows how you can define a Triton deployment to deploy to a managed
     endpoint_name = f"endpoint-{random.randint(0, 10000)}"
     ```
 
-1. We use these details above in the `MLClient` from `azure.ai.ml` to get a handle to the required Azure Machine Learning workspace. Check the [configuration notebook](https://github.com/Azure/azureml-examples/tree/main/sdk/jobs/configuration.ipynb) for more details on how to configure credentials and connect to a workspace.
+1. We use these details above in the `MLClient` from `azure.ai.ml` to get a handle to the required Azure Machine Learning workspace. Check the [configuration notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/configuration.ipynb) for more details on how to configure credentials and connect to a workspace.
 
     ```python 
     from azure.ai.ml import MLClient
@@ -352,7 +352,7 @@ Once your deployment completes, use the following command to make a scoring requ
     keys = ml_client.online_endpoints.list_keys(endpoint_name)
     auth_key = keys.primary_key
 
-1. The following scoring code uses the [Triton Inference Server Client](https://github.com/triton-inference-server/client) to submit the image of a peacock to the endpoint. This script is available in the companion notebook to this example - [Deploy a model to online endpoints using Triton](https://github.com/Azure/azureml-examples/blob/main/sdk/endpoints/online/triton/single-model/online-endpoints-triton.ipynb).
+1. The following scoring code uses the [Triton Inference Server Client](https://github.com/triton-inference-server/client) to submit the image of a peacock to the endpoint. This script is available in the companion notebook to this example - [Deploy a model to online endpoints using Triton](https://github.com/Azure/azureml-examples/blob/main/sdk/python/endpoints/online/triton/single-model/online-endpoints-triton.ipynb).
 
     ```python
     # Test the blue deployment with some sample data