updates for v2 SDK links

Author: Larry Franks

articles/machine-learning/concept-automated-ml.md

@@ -23,7 +23,7 @@ Traditional machine learning model development is resource-intensive, requiring
 
 Azure Machine Learning offers the following two experiences for working with automated ML. See the following sections to understand feature availability in each experience.
 
-* For code-experienced customers, [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/intro).  Get started with [Tutorial: Train an object detection model (preview) with AutoML and Python](tutorial-auto-train-image-models.md)
+* For code-experienced customers, [Azure Machine Learning Python SDK](https://aka.ms/sdk-v2-install).  Get started with [Tutorial: Train an object detection model (preview) with AutoML and Python](tutorial-auto-train-image-models.md)
 
 * For limited/no-code experience customers, Azure Machine Learning studio at [https://ml.azure.com](https://ml.azure.com/).  Get started with these tutorials:
     * [Tutorial: Create a classification model with automated ML in Azure Machine Learning](tutorial-first-experiment-automated-ml.md).

articles/machine-learning/how-to-machine-learning-interpretability.md

@@ -59,7 +59,8 @@ By using the classes and methods in the SDK v1, you can:
 * Achieve model interpretability on real-world datasets at scale during training and inference.
 * Use an interactive visualization dashboard to discover patterns in your data and its explanations at training time.
 
-Model interpretability classes are made available through the SDK v1 package. For more information, see [Install SDK packages for Azure Machine Learning](/python/api/overview/azure/ml/install) and [azureml.interpret](/python/api/azureml-interpret/azureml.interpret).
+> [!NOTE]
+> Model interpretability classes are made available through the SDK v1 package. For more information, see [Install SDK packages for Azure Machine Learning](/python/api/overview/azure/ml/install) and [azureml.interpret](/python/api/azureml-interpret/azureml.interpret).
 
 ## Supported model interpretability techniques
 

articles/machine-learning/how-to-manage-optimize-cost.md

@@ -53,7 +53,7 @@ You can also configure the amount of time the node is idle before scale down. By
 + If you perform less iterative experimentation, reduce this time to save costs.
 + If you perform highly iterative dev/test experimentation, you might need to increase the time so you aren't paying for constant scaling up and down after each change to your training script or environment.
 
-AmlCompute clusters can be configured for your changing workload requirements in Azure portal, using the [AmlCompute SDK class](/python/api/azureml-core/azureml.core.compute.amlcompute.amlcompute), [AmlCompute CLI](/cli/azure/ml(v1)/computetarget/create#az-ml-v1--computetarget-create-amlcompute), with the [REST APIs](https://github.com/Azure/azure-rest-api-specs/tree/master/specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable).
+AmlCompute clusters can be configured for your changing workload requirements in Azure portal, using the [AmlCompute SDK class](/python/api/azure-ai-ml/azure.ai.ml.entities.amlcompute), [AmlCompute CLI](/cli/azure/ml/compute#az-ml-compute-create), with the [REST APIs](https://github.com/Azure/azure-rest-api-specs/tree/master/specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable).
 
 
 ## Set quotas on resources

articles/machine-learning/how-to-private-endpoint-integration-synapse.md

@@ -135,7 +135,8 @@ To verify that the integration between Azure Synapse and Azure Machine Learning
     print(ws.name)
     ```
 
-    This code snippet connects to the linked workspace, and then prints the workspace info. In the printed output, the value displayed is the name of the Azure Machine Learning workspace, not the linked service name that was used in the `getWorkspace()` call. For more information on using the `ws` object, see the [Workspace](/python/api/azureml-core/azureml.core.workspace.workspace) class reference.
+    > [!IMPORTANT]
+    > This code snippet connects to the linked workspace using SDK v1, and then prints the workspace info. In the printed output, the value displayed is the name of the Azure Machine Learning workspace, not the linked service name that was used in the `getWorkspace()` call. For more information on using the `ws` object, see the [Workspace](/python/api/azureml-core/azureml.core.workspace.workspace) class reference.
 
 ## Next steps
 

articles/machine-learning/how-to-setup-customer-managed-keys.md

@@ -131,6 +131,9 @@ For more information on customer-managed keys with Cosmos DB, see [Configure cus
 
 ### Azure Container Instance
 
+> [!IMPORTANT]
+> Deploying to Azure Container Instances is not available in SDK or CLI v2. Only through SDK & CL v1.
+
 When __deploying__ a trained model to an Azure Container instance (ACI), you can encrypt the deployed resource using a customer-managed key. For information on generating a key, see [Encrypt data with a customer-managed key](../container-instances/container-instances-encrypt-data.md#generate-a-new-key).
 
 To use the key when deploying a model to Azure Container Instance, create a new deployment configuration using `AciWebservice.deploy_configuration()`. Provide the key information using the following parameters:

articles/machine-learning/how-to-tune-hyperparameters.md

@@ -423,7 +423,7 @@ To see how the parameter values are received, parsed, and passed to the training
 
 ## Submit hyperparameter tuning experiment
 
-After you define your hyperparameter tuning configuration, [submit the experiment](/python/api/azureml-core/azureml.core.experiment%28class%29#submit-config--tags-none----kwargs-):
+After you define your hyperparameter tuning configuration, [submit the job](/python/api/azure-ai-ml/azure.ai.ml.mlclient#azure-ai-ml-mlclient-create-or-update):
 
 ```Python
 # submit the sweep

articles/machine-learning/toc.yml

@@ -282,13 +282,6 @@
           href: how-to-prevent-data-loss-exfiltration.md
         - name: Configure network isolation with v2
           href: how-to-configure-network-isolation-with-v2.md
-    - name: Data protection
-      items: 
-        - name: Failover & disaster recovery
-          displayName: high availability
-          href: how-to-high-availability-machine-learning.md
-        - name: Regenerate storage access keys
-          href: how-to-change-storage-access-key.md
     - name: Create & manage workspaces
       items:
         - name: Use Azure portal or Python SDK
@@ -503,8 +496,6 @@
               href: how-to-auto-train-nlp-models.md
             - name: Understand charts and metrics
               href: how-to-understand-automated-ml.md
-            - name: Generate AutoML training code
-              href: how-to-generate-automl-training-code.md
             - name: Use ONNX model in .NET application
               href: how-to-use-automl-onnx-model-dotnet.md
             - name: Inference image models with ONNX model

articles/machine-learning/tutorial-1st-experiment-sdk-train.md

@@ -152,7 +152,7 @@ After the script completes, select **Refresh** above the file folders. You'll se
 
 ## Create a Python environment
 
-Azure Machine Learning provides the concept of an [environment](/python/api/azureml-core/azureml.core.environment.environment) to represent a reproducible, versioned Python environment for running experiments. It's easy to create an environment from a local Conda or pip environment.
+Azure Machine Learning provides the concept of an [environment](/python/api/azure-ai-ml/azure.ai.ml.entities.environment) to represent a reproducible, versioned Python environment for running experiments. It's easy to create an environment from a local Conda or pip environment.
 
 First you'll create a file with the package dependencies.
 
@@ -407,8 +407,6 @@ This time when you visit the studio, go to the **Metrics** tab where you can now
 
 In this session, you upgraded from a basic "Hello world!" script to a more realistic training script that required a specific Python environment to run. You saw how to use curated Azure Machine Learning environments. Finally, you saw how in a few lines of code you can log metrics to Azure Machine Learning.
 
-There are other ways to create Azure Machine Learning environments, including [from a pip requirements.txt](/python/api/azureml-core/azureml.core.environment.environment#from-pip-requirements-name--file-path-) file or [from an existing local Conda environment](/python/api/azureml-core/azureml.core.environment.environment#from-existing-conda-environment-name--conda-environment-name-).
-
 In the next session, you'll see how to work with data in Azure Machine Learning by uploading the CIFAR10 dataset to Azure.
 
 > [!div class="nextstepaction"]

articles/machine-learning/v1/toc.yml

@@ -153,6 +153,13 @@
       - name: Configure secure web services (v1)
         displayName: ssl, tls
         href: how-to-secure-web-service.md
+    - name: Data protection
+      items: 
+      - name: Failover & disaster recovery
+        displayName: high availability
+        href: how-to-high-availability-machine-learning.md
+      - name: Regenerate storage access keys
+        href: how-to-change-storage-access-key.md
 - name: How-to guides (v1)
   items:
   - name: Install and set up the CLI (v1)
@@ -268,6 +275,8 @@
       - name: Data splits & cross-validation (Python)
         displayName: automl, feature engineering, feature importance
         href: ../how-to-configure-cross-validation-data-splits.md
+      - name: Generate AutoML training code
+        href: how-to-generate-automl-training-code.md
       - name: Featurization in automated ML (Python)
         displayName: automl, feature engineering, feature importance, BERT
         href: ../how-to-configure-auto-features.md