Merge pull request #203275 from Blackmist/remove-aml

v-shils · web-flow · commit 05e8fef3c4dd · 2022-06-29T08:00:29.000-07:00
bulk update to change terms where possible
diff --git a/articles/machine-learning/concept-workspace.md b/articles/machine-learning/concept-workspace.md
@@ -96,9 +96,9 @@ There are multiple ways to create a workspace:
 
 ## <a name="sub-resources"></a> Sub resources
 
-These sub resources are the main resources that are made in the AML workspace.
+These sub resources are the main resources that are made in the AzureML workspace.
 
-* VMs: provide computing power for your AML workspace and are an integral part in deploying and training models.
+* VMs: provide computing power for your AzureML workspace and are an integral part in deploying and training models.
 * Load Balancer: a network load balancer is created for each compute instance and compute cluster to manage traffic even while the compute instance/cluster is stopped.
 * Virtual Network: these help Azure resources communicate with one another, the internet, and other on-premises networks.
 * Bandwidth: encapsulates all outbound data transfers across regions.
diff --git a/articles/machine-learning/how-to-configure-auto-train.md b/articles/machine-learning/how-to-configure-auto-train.md
@@ -65,10 +65,10 @@ try:
     ml_client = MLClient.from_config(credential)
 except Exception as ex:
     print(ex)
-    # Enter details of your AML workspace
+    # Enter details of your AzureML workspace
     subscription_id = "<SUBSCRIPTION_ID>"
     resource_group = "<RESOURCE_GROUP>"
-    workspace = "<AML_WORKSPACE_NAME>"
+    workspace = "<AZUREML_WORKSPACE_NAME>"
     ml_client = MLClient(credential, subscription_id, resource_group, workspace)
 
 ```
diff --git a/articles/machine-learning/how-to-configure-databricks-automl-environment.md b/articles/machine-learning/how-to-configure-databricks-automl-environment.md
@@ -92,7 +92,7 @@ To use automated ML, skip to [Add the Azure ML SDK with AutoML](#add-the-azure-m
   ![Azure Machine Learning SDK for Databricks](./media/how-to-configure-environment/amlsdk-withoutautoml.jpg) 
 
 ## Add the Azure ML SDK with AutoML to Databricks
-If the cluster was created with Databricks Runtime 7.3 LTS (*not* ML), run the following command in the first cell of your notebook to install the AML SDK.
+If the cluster was created with Databricks Runtime 7.3 LTS (*not* ML), run the following command in the first cell of your notebook to install the AzureML SDK.
 
 ```
 %pip install --upgrade --force-reinstall -r https://aka.ms/automl_linux_requirements.txt
diff --git a/articles/machine-learning/how-to-create-attach-kubernetes.md b/articles/machine-learning/how-to-create-attach-kubernetes.md
@@ -29,17 +29,17 @@ Azure Machine Learning can deploy trained machine learning models to Azure Kuber
 
 ## Limitations
 
-- If you need a **Standard Load Balancer(SLB)** deployed in your cluster instead of a Basic Load Balancer(BLB), create a cluster in the AKS portal/CLI/SDK and then **attach** it to the AML workspace.
+- If you need a **Standard Load Balancer(SLB)** deployed in your cluster instead of a Basic Load Balancer(BLB), create a cluster in the AKS portal/CLI/SDK and then **attach** it to the AzureML workspace.
 
 - If you have an Azure Policy that restricts the creation of Public IP addresses, then AKS cluster creation will fail. AKS requires a Public IP for [egress traffic](../aks/limit-egress-traffic.md). The egress traffic article also provides guidance to lock down egress traffic from the cluster through the Public IP, except for a few fully qualified domain names. There are 2 ways to enable a Public IP:
     - The cluster can use the Public IP created by default with the BLB or SLB, Or
     - The cluster can be created without a Public IP and then a Public IP is configured with a firewall with a user defined route. For more information, see [Customize cluster egress with a user-defined-route](../aks/egress-outboundtype.md).
     
-    The AML control plane does not talk to this Public IP. It talks to the AKS control plane for deployments. 
+    The AzureML control plane does not talk to this Public IP. It talks to the AKS control plane for deployments. 
 
 - To attach an AKS cluster, the service principal/user performing the operation must be assigned the __Owner or contributor__ Azure role-based access control (Azure RBAC) role on the Azure resource group that contains the cluster. The service principal/user must also be assigned [Azure Kubernetes Service Cluster Admin Role](../role-based-access-control/built-in-roles.md#azure-kubernetes-service-cluster-admin-role) on the cluster.
 
-- If you **attach** an AKS cluster, which has an [Authorized IP range enabled to access the API server](../aks/api-server-authorized-ip-ranges.md), enable the AML control plane IP ranges for the AKS cluster. The AML control plane is deployed across paired regions and deploys inference pods on the AKS cluster. Without access to the API server, the inference pods cannot be deployed. Use the [IP ranges](https://www.microsoft.com/download/confirmation.aspx?id=56519) for both the [paired regions](../availability-zones/cross-region-replication-azure.md) when enabling the IP ranges in an AKS cluster.
+- If you **attach** an AKS cluster, which has an [Authorized IP range enabled to access the API server](../aks/api-server-authorized-ip-ranges.md), enable the AzureML control plane IP ranges for the AKS cluster. The AzureML control plane is deployed across paired regions and deploys inference pods on the AKS cluster. Without access to the API server, the inference pods cannot be deployed. Use the [IP ranges](https://www.microsoft.com/download/confirmation.aspx?id=56519) for both the [paired regions](../availability-zones/cross-region-replication-azure.md) when enabling the IP ranges in an AKS cluster.
 
     Authorized IP ranges only works with Standard Load Balancer.
 
diff --git a/articles/machine-learning/how-to-create-component-pipeline-python.md b/articles/machine-learning/how-to-create-component-pipeline-python.md
@@ -157,7 +157,7 @@ The `train.py` file contains a normal python function, which performs the traini
 
 #### Define component using python function
 
-After defining the training function successfully, you can use @command_component in Azure Machine Learning SDK v2 to wrap your function as a component, which can be used in AML pipelines.
+After defining the training function successfully, you can use @command_component in Azure Machine Learning SDK v2 to wrap your function as a component, which can be used in AzureML pipelines.
 
 :::code language="python" source="~/azureml-examples-main/sdk/jobs/pipelines/2e_image_classification_keras_minist_convnet/train/train_component.py":::
 
diff --git a/articles/machine-learning/how-to-data-ingest-adf.md b/articles/machine-learning/how-to-data-ingest-adf.md
@@ -117,7 +117,7 @@ The following Python code demonstrates how to create a datastore that connects t
 
 ```python
 ws = Workspace.from_config()
-adlsgen2_datastore_name = '<ADLS gen2 storage account alias>'  #set ADLS Gen2 storage account alias in AML
+adlsgen2_datastore_name = '<ADLS gen2 storage account alias>'  #set ADLS Gen2 storage account alias in AzureML
 
 subscription_id=os.getenv("ADL_SUBSCRIPTION", "<ADLS account subscription ID>") # subscription id of ADLS account
 resource_group=os.getenv("ADL_RESOURCE_GROUP", "<ADLS account resource group>") # resource group of ADLS account
@@ -147,7 +147,7 @@ from azureml.core import Workspace, Datastore, Dataset
 from azureml.core.experiment import Experiment
 from azureml.train.automl import AutoMLConfig
 
-# retrieve data via AML datastore
+# retrieve data via AzureML datastore
 datastore = Datastore.get(ws, adlsgen2_datastore)
 datastore_path = [(datastore, '/data/prepared-data.csv')]
         
diff --git a/articles/machine-learning/how-to-debug-visual-studio-code.md b/articles/machine-learning/how-to-debug-visual-studio-code.md
@@ -138,7 +138,7 @@ To enable debugging, make the following changes to the Python script(s) used by
     parser.add_argument('--remote_debug', action='store_true')
     parser.add_argument('--remote_debug_connection_timeout', type=int,
                         default=300,
-                        help=f'Defines how much time the AML compute target '
+                        help=f'Defines how much time the AzureML compute target '
                         f'will await a connection from a debugger client (VSCODE).')
     parser.add_argument('--remote_debug_client_ip', type=str,
                         help=f'Defines IP Address of VS Code client')
@@ -195,7 +195,7 @@ parser.add_argument("--output_train", type=str, help="output_train directory")
 parser.add_argument('--remote_debug', action='store_true')
 parser.add_argument('--remote_debug_connection_timeout', type=int,
                     default=300,
-                    help=f'Defines how much time the AML compute target '
+                    help=f'Defines how much time the AzureML compute target '
                     f'will await a connection from a debugger client (VSCODE).')
 parser.add_argument('--remote_debug_client_ip', type=str,
                     help=f'Defines IP Address of VS Code client')
diff --git a/articles/machine-learning/how-to-deploy-managed-online-endpoint-sdk-v2.md b/articles/machine-learning/how-to-deploy-managed-online-endpoint-sdk-v2.md
@@ -68,10 +68,10 @@ The [workspace](concept-workspace.md) is the top-level resource for Azure Machin
     To connect to a workspace, we need identifier parameters - a subscription, resource group and workspace name. We'll use these details in the `MLClient` from `azure.ai.ml` to get a handle to the required Azure Machine Learning workspace. This example uses the [default Azure authentication](/python/api/azure-identity/azure.identity.defaultazurecredential).
 
     ```python
-    # enter details of your AML workspace
+    # enter details of your AzureML workspace
     subscription_id = "<SUBSCRIPTION_ID>"
     resource_group = "<RESOURCE_GROUP>"
-    workspace = "<AML_WORKSPACE_NAME>"
+    workspace = "<AZUREML_WORKSPACE_NAME>"
     ```
 
     ```python
diff --git a/articles/machine-learning/how-to-deploy-model-cognitive-search.md b/articles/machine-learning/how-to-deploy-model-cognitive-search.md
@@ -223,7 +223,7 @@ from azureml.core.webservice import AksWebservice, Webservice
 
 # If deploying to a cluster configured for dev/test, ensure that it was created with enough
 # cores and memory to handle this deployment configuration. Note that memory is also used by
-# things such as dependencies and AML components.
+# things such as dependencies and AzureML components.
 
 aks_config = AksWebservice.deploy_configuration(autoscale_enabled=True, 
                                                        autoscale_min_replicas=1, 
diff --git a/articles/machine-learning/how-to-generate-automl-training-code.md b/articles/machine-learning/how-to-generate-automl-training-code.md
@@ -440,7 +440,7 @@ run = experiment.submit(config=src)
 
 Once you have a trained model, you can save/serialize it to a `.pkl` file with  `pickle.dump()` and `pickle.load()`.  You can also use `joblib.dump()` and `joblib.load()`.
 
-The following example is how you download and load  a model in-memory that was trained in AML compute with `ScriptRunConfig`. This code can run in the same notebook you used the Azure ML SDK `ScriptRunConfig`.
+The following example is how you download and load  a model in-memory that was trained in AzureML compute with `ScriptRunConfig`. This code can run in the same notebook you used the Azure ML SDK `ScriptRunConfig`.
 
 ```python
 import joblib
diff --git a/articles/machine-learning/how-to-safely-rollout-managed-endpoints-sdk-v2.md b/articles/machine-learning/how-to-safely-rollout-managed-endpoints-sdk-v2.md
@@ -78,10 +78,10 @@ The [workspace](concept-workspace.md) is the top-level resource for Azure Machin
     To connect to a workspace, we need identifier parameters - a subscription, resource group and workspace name. We'll use these details in the `MLClient` from `azure.ai.ml` to get a handle to the required Azure Machine Learning workspace. This example uses the [default Azure authentication](/python/api/azure-identity/azure.identity.defaultazurecredential).
 
     ```python
-    # enter details of your AML workspace
+    # enter details of your AzureML workspace
     subscription_id = "<SUBSCRIPTION_ID>"
     resource_group = "<RESOURCE_GROUP>"
-    workspace = "<AML_WORKSPACE_NAME>"
+    workspace = "<AZUREML_WORKSPACE_NAME>"
     ```
 
     ```python
diff --git a/articles/machine-learning/how-to-secure-web-service.md b/articles/machine-learning/how-to-secure-web-service.md
@@ -70,7 +70,7 @@ When you request a certificate, you must provide the FQDN of the address that yo
 
 ## <a id="enable"></a> Enable TLS and deploy
 
-**For AKS deployment**, you can enable TLS termination when you [create or attach an AKS cluster](how-to-create-attach-kubernetes.md) in AML workspace. At AKS model deployment time, you can disable TLS termination with deployment configuration object, otherwise all AKS model deployment by default will have TLS termination enabled at AKS cluster create or attach time.
+**For AKS deployment**, you can enable TLS termination when you [create or attach an AKS cluster](how-to-create-attach-kubernetes.md) in AzureML workspace. At AKS model deployment time, you can disable TLS termination with deployment configuration object, otherwise all AKS model deployment by default will have TLS termination enabled at AKS cluster create or attach time.
 
 For ACI deployment, you can enable TLS termination at model deployment time with deployment configuration object.
 
@@ -80,7 +80,7 @@ For ACI deployment, you can enable TLS termination at model deployment time with
   > [!NOTE]
   > The information in this section also applies when you deploy a secure web service for the designer. If you aren't familiar with using the Python SDK, see [What is the Azure Machine Learning SDK for Python?](/python/api/overview/azure/ml/intro).
 
-When you [create or attach an AKS cluster](how-to-create-attach-kubernetes.md) in AML workspace, you can enable TLS termination with **[AksCompute.provisioning_configuration()](/python/api/azureml-core/azureml.core.compute.akscompute#provisioning-configuration-agent-count-none--vm-size-none--ssl-cname-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--location-none--vnet-resourcegroup-name-none--vnet-name-none--subnet-name-none--service-cidr-none--dns-service-ip-none--docker-bridge-cidr-none--cluster-purpose-none--load-balancer-type-none--load-balancer-subnet-none-)** and **[AksCompute.attach_configuration()](/python/api/azureml-core/azureml.core.compute.akscompute#attach-configuration-resource-group-none--cluster-name-none--resource-id-none--cluster-purpose-none-)** configuration objects. Both methods return a configuration object that has an **enable_ssl** method, and you can use **enable_ssl** method to enable TLS.
+When you [create or attach an AKS cluster](how-to-create-attach-kubernetes.md) in AzureML workspace, you can enable TLS termination with **[AksCompute.provisioning_configuration()](/python/api/azureml-core/azureml.core.compute.akscompute#provisioning-configuration-agent-count-none--vm-size-none--ssl-cname-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--location-none--vnet-resourcegroup-name-none--vnet-name-none--subnet-name-none--service-cidr-none--dns-service-ip-none--docker-bridge-cidr-none--cluster-purpose-none--load-balancer-type-none--load-balancer-subnet-none-)** and **[AksCompute.attach_configuration()](/python/api/azureml-core/azureml.core.compute.akscompute#attach-configuration-resource-group-none--cluster-name-none--resource-id-none--cluster-purpose-none-)** configuration objects. Both methods return a configuration object that has an **enable_ssl** method, and you can use **enable_ssl** method to enable TLS.
 
 You can enable TLS either with Microsoft certificate or a custom certificate purchased from CA. 
 
diff --git a/articles/machine-learning/how-to-train-sdk.md b/articles/machine-learning/how-to-train-sdk.md
@@ -95,10 +95,10 @@ To connect to the workspace, you need identifier parameters - a subscription, re
 from azure.ai.ml import MLClient
 from azure.identity import DefaultAzureCredential
 
-#Enter details of your AML workspace
+#Enter details of your AzureML workspace
 subscription_id = '<SUBSCRIPTION_ID>'
 resource_group = '<RESOURCE_GROUP>'
-workspace = '<AML_WORKSPACE_NAME>'
+workspace = '<AZUREML_WORKSPACE_NAME>'
 
 #connect to the workspace
 ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group, workspace)
diff --git a/articles/machine-learning/how-to-use-automated-ml-for-ml-models.md b/articles/machine-learning/how-to-use-automated-ml-for-ml-models.md
@@ -101,7 +101,7 @@ Otherwise, you'll see a list of your recent automated  ML experiments, including
     Virtual machine priority| Low priority virtual machines are cheaper but don't guarantee the compute nodes. 
     Virtual machine type| Select CPU or GPU for virtual machine type.
     Virtual machine size| Select the virtual machine size for your compute.
-    Min / Max nodes| To profile data, you must specify 1 or more nodes. Enter the maximum number of nodes for your compute. The default is 6 nodes for an AML Compute.
+    Min / Max nodes| To profile data, you must specify 1 or more nodes. Enter the maximum number of nodes for your compute. The default is 6 nodes for an AzureML Compute.
     Advanced settings | These settings allow you to configure a user account and existing virtual network for your experiment. 
     
     Select **Create**. Creation of a new compute can take a few minutes.
diff --git a/articles/machine-learning/how-to-use-automlstep-in-pipelines.md b/articles/machine-learning/how-to-use-automlstep-in-pipelines.md
@@ -98,7 +98,7 @@ compute_target = ws.compute_targets[compute_name]
 
 The intermediate data between the data preparation and the automated ML step can be stored in the workspace's default datastore, so we don't need to do more than call `get_default_datastore()` on the `Workspace` object. 
 
-After that, the code checks if the AML compute target `'cpu-cluster'` already exists. If not, we specify that we want a small CPU-based compute target. If you plan to use automated ML's deep learning features (for instance, text featurization with DNN support) you should choose a compute with strong GPU support, as described in [GPU optimized virtual machine sizes](../virtual-machines/sizes-gpu.md). 
+After that, the code checks if the AzureML compute target `'cpu-cluster'` already exists. If not, we specify that we want a small CPU-based compute target. If you plan to use automated ML's deep learning features (for instance, text featurization with DNN support) you should choose a compute with strong GPU support, as described in [GPU optimized virtual machine sizes](../virtual-machines/sizes-gpu.md). 
 
 The code blocks until the target is provisioned and then prints some details of the just-created compute target. Finally, the named compute target is retrieved from the workspace and assigned to `compute_target`. 
 
diff --git a/articles/machine-learning/how-to-use-batch-endpoint-sdk-v2.md b/articles/machine-learning/how-to-use-batch-endpoint-sdk-v2.md
@@ -67,10 +67,10 @@ The [workspace](concept-workspace.md) is the top-level resource for Azure Machin
     To connect to a workspace, we need identifier parameters - a subscription, resource group and workspace name. We'll use these details in the `MLClient` from `azure.ai.ml` to get a handle to the required Azure Machine Learning workspace. This example uses the [default Azure authentication](/python/api/azure-identity/azure.identity.defaultazurecredential).
 
     ```python
-    # enter details of your AML workspace
+    # enter details of your AzureML workspace
     subscription_id = "<SUBSCRIPTION_ID>"
     resource_group = "<RESOURCE_GROUP>"
-    workspace = "<AML_WORKSPACE_NAME>"
+    workspace = "<AZUREML_WORKSPACE_NAME>"
     ```
 
     ```python
diff --git a/articles/machine-learning/how-to-use-batch-endpoints-studio.md b/articles/machine-learning/how-to-use-batch-endpoints-studio.md
@@ -101,7 +101,7 @@ OR
 
 * Use a **datastore**:
 
-    You can specify AML registered datastore or if your data is publicly available, specify the public path.
+    You can specify AzureML registered datastore or if your data is publicly available, specify the public path.
 
     :::image type="content" source="media/how-to-use-batch-endpoints-studio/select-datastore-job.png" alt-text="Screenshot of selecting datastore as an input option":::
 
diff --git a/articles/machine-learning/how-to-use-mlflow-cli-runs.md b/articles/machine-learning/how-to-use-mlflow-cli-runs.md
@@ -59,7 +59,7 @@ To track a run that is not running on Azure Machine Learning compute (from now o
 > [!NOTE]
 > When running on Azure Compute (Azure Notebooks, Jupyter Notebooks hosted on Azure Compute Instances or Compute Clusters) you don't have to configure the tracking URI. It's automatically configured for you.
 
-# [Using the Azure ML SDK v2](#tab/amlsdk)
+# [Using the Azure ML SDK v2](#tab/azuremlsdk)
 
 You can get the Azure ML MLflow tracking URI using the [Azure Machine Learning SDK v2 for Python](concept-v2.md). Ensure you have the library `azure-ai-ml` installed in the cluster you are using. The following sample gets the unique MLFLow tracking URI associated with your workspace. Then the method [`set_tracking_uri()`](https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.set_tracking_uri) points the MLflow tracking URI to that URI.
 
@@ -68,10 +68,10 @@ from azure.ai.ml import MLClient
 from azure.identity import DefaultAzureCredential
 import mlflow
 
-#Enter details of your AML workspace
+#Enter details of your AzureML workspace
 subscription_id = '<SUBSCRIPTION_ID>'
 resource_group = '<RESOURCE_GROUP>'
-workspace = '<AML_WORKSPACE_NAME>'
+workspace = '<AZUREML_WORKSPACE_NAME>'
 
 ml_client = MLClient(credential=DefaultAzureCredential(),
                      subscription_id=subscription_id, 
diff --git a/articles/machine-learning/monitor-resource-reference.md b/articles/machine-learning/monitor-resource-reference.md
diff --git a/articles/machine-learning/reference-automl-images-schema.md b/articles/machine-learning/reference-automl-images-schema.md
diff --git a/articles/machine-learning/reference-machine-learning-cloud-parity.md b/articles/machine-learning/reference-machine-learning-cloud-parity.md
