Merge pull request #213457 from sdgilley/sdg-main-updates

remove v1 code from articles

Author: v-stsavell

articles/machine-learning/concept-mlflow-models.md

@@ -26,8 +26,6 @@ Any file generated (and captured) from an experiment's run or job is an artifact
 
 You may have been logging artifacts already:
 
-# [Using MLflow SDK](#tab/mlflow)
-
 ```python
 filename = 'model.pkl'
 with open(filename, 'wb') as f:
@@ -36,32 +34,6 @@ with open(filename, 'wb') as f:
 mlflow.log_artifact(filename)
 ```
 
-# [Using Azure ML SDK v1](#tab/sdkv1)
-
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
-
-```python
-filename = 'model.pkl'
-with open(filename, 'wb') as f:
-  pickle.dump(model, f)
-
-mlflow.log_file(filename)
-```
-
-# [Using the outputs folder](#tab/outputs)
-
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
-
-```python
-os.mkdirs("outputs", exists_ok=True)
-
-filename = 'outputs/model.pkl'
-with open(filename, 'wb') as f:
-  pickle.dump(model, f)
-```
-
----
-
 ### Models
 
 A model in MLflow is also an artifact. However, we make stronger assumptions about this type of artifacts. Such assumptions provide a clear contract between the saved files and what they mean. When you log your models as artifacts (simple files), you need to know what the model builder meant for each of them in order to know how to load the model for inference. On the contrary, MLflow models can be loaded using the contract specified in the [The MLModel format](concept-mlflow-models.md#the-mlmodel-format).
@@ -73,33 +45,13 @@ In Azure Machine Learning, logging models has the following advantages:
 > * Models can be used as pipelines inputs directly.
 > * You can use the [Responsible AI dashbord (preview)](how-to-responsible-ai-dashboard.md).
 
-Models can get logged by:
-
-# [Using MLflow SDK](#tab/mlflow)
+Models can get logged by using MLflow SDK:
 
 ```python
 import mlflow
 mlflow..sklearn.log_model(sklearn_estimator, "classifier")
 ```
 
-# [Using Azure ML SDK v1](#tab/sdkv1)
-
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
-
-> [!IMPORTANT]
-> Azure ML SDK v1 doesn't have the *model* concept.
-
-# [Using the outputs folder](#tab/outputs)
-
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
-
-```python
-os.mkdirs("outputs/classifier", exists_ok=True)
-
-mlflow.sklearn.save_model(sklearn_estimator, "outputs/classifier")
-```
-
----
 
 ## The MLmodel format
 

articles/machine-learning/how-to-attach-kubernetes-to-workspace.md

@@ -61,32 +61,7 @@ Set the `--type` argument to `Kubernetes`. Use the `identity_type` argument to e
 > `--user-assigned-identities` is only required for `UserAssigned` managed identities. Although you can provide a list of comma-separated user managed identities, only the first one is used when you attach your cluster.
 >
 > Compute attach won't create the Kubernetes namespace automatically or validate whether the kubernetes namespace existed. You need to verify that the specified namespace exists in your cluster, otherwise, any AzureML workloads submitted to this compute will fail.  
-### [Python SDK](#tab/python)
 
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
-
-```python
-from azureml.core.compute import KubernetesCompute, ComputeTarget
-
-# Specify a name for your Kubernetes compute
-compute_target_name = "<kubernetes compute target name>"
-
-# resource ID of the Arc-enabled Kubernetes cluster
-cluster_resource_id = "/subscriptions/<sub ID>/resourceGroups/<RG>/providers/Microsoft.Kubernetes/connectedClusters/<cluster name>"
-
-user_assigned_identity_resouce_id = ['subscriptions/<sub ID>/resourceGroups/<RG>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<identity name>']
-
-# Specify Kubernetes namespace to run AzureML workloads
-ns = "default" 
-
-try:
-    compute_target = ComputeTarget(workspace=ws, name=compute_target_name)
-    print('Found existing cluster, use it.')
-except ComputeTargetException:
-    attach_configuration = KubernetesCompute.attach_configuration(resource_id = cluster_resource_id, namespace = ns,  identity_type ='UserAssigned',identity_ids = user_assigned_identity_resouce_id)
-    compute_target = ComputeTarget.attach(ws, compute_target_name, attach_configuration)
-    compute_target.wait_for_completion(show_output=True)
-```
 ### [Studio](#tab/studio)
 
 Attaching a Kubernetes cluster makes it available to your workspace for training or inferencing.