misc updates for v2

Author: Larry Franks

articles/machine-learning/concept-compute-target.md

@@ -9,7 +9,7 @@ ms.topic: conceptual
 ms.author: sgilley
 author: sdgilley
 ms.date: 10/21/2021
-ms.custom: ignite-fall-2021, event-tier1-build-2022
+ms.custom: ignite-fall-2021, event-tier1-build-2022, cliv2
 #Customer intent: As a data scientist, I want to understand what a compute target is and why I need it.
 ---
 
@@ -116,8 +116,7 @@ While Azure Machine Learning supports these VM series, they might not be availab
 > [!NOTE]
 > Azure Machine Learning doesn't support all VM sizes that Azure Compute supports. To list the available VM sizes, use one of the following methods:
 > * [REST API](https://github.com/Azure/azure-rest-api-specs/blob/master/specification/machinelearningservices/resource-manager/Microsoft.MachineLearningServices/stable/2020-08-01/examples/ListVMSizesResult.json)
-> * [Python SDK](/python/api/azureml-core/azureml.core.compute.amlcompute.amlcompute#azureml-core-compute-amlcompute-amlcompute-supported-vmsizes)
->
+> * The [Azure CLI extension 2.0 for machine learning](how-to-configure-cli.md) command, [az ml compute list-sizes](/cli/azure/ml/compute#az-ml-compute-list-sizes).
 
 If using the GPU-enabled compute targets, it is important to ensure that the correct CUDA drivers are installed in the training environment. Use the following table to determine the correct CUDA version to use:
 

articles/machine-learning/concept-data-encryption.md

@@ -63,6 +63,9 @@ For an example of creating a workspace using an existing Azure Container Registr
 
 ### Azure Container Instance
 
+> [!IMPORTANT]
+> Deployments to ACI rely on the Azure Machine Learning Python SDK and CLI v1.
+
 You may encrypt a deployed Azure Container Instance (ACI) resource using customer-managed keys. The customer-managed key used for ACI can be stored in the Azure Key Vault for your workspace. 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).
 
 [!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
@@ -77,7 +80,7 @@ For more information on creating and using a deployment configuration, see the f
 
 * [AciWebservice.deploy_configuration()](/python/api/azureml-core/azureml.core.webservice.aci.aciwebservice#deploy-configuration-cpu-cores-none--memory-gb-none--tags-none--properties-none--description-none--location-none--auth-enabled-none--ssl-enabled-none--enable-app-insights-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--ssl-cname-none--dns-name-label-none--primary-key-none--secondary-key-none--collect-model-data-none--cmk-vault-base-url-none--cmk-key-name-none--cmk-key-version-none-) reference
 
-* [Where and how to deploy](how-to-deploy-managed-online-endpoints.md)
+* [Where and how to deploy](./v1/how-to-deploy-and-where.md)
 
 For more information on using a customer-managed key with ACI, see [Encrypt deployment data](../container-instances/container-instances-encrypt-data.md).
 

articles/machine-learning/concept-environments.md

@@ -72,13 +72,13 @@ If the image for a particular environment definition doesn't already exist in th
  1. Downloading a base image, and executing any Docker steps
  2. Building a conda environment according to conda dependencies specified in the environment definition.
 
-The second step is omitted if you specify [user-managed dependencies](/python/api/azureml-core/azureml.core.environment.pythonsection). In this case you're responsible for installing any Python packages, by including them in your base image, or specifying custom Docker steps within the first step. You're also responsible for specifying the correct location for the Python executable. It is also possible to use a [custom Docker base image](./how-to-deploy-custom-container.md).
+The second step is optional, and the environment may instead come from the Docker build context or base image. In this case you're responsible for installing any Python packages, by including them in your base image, or specifying custom Docker steps. You're also responsible for specifying the correct location for the Python executable. It is also possible to use a [custom Docker base image](./how-to-deploy-custom-container.md).
 
 ### Image caching and reuse
 
 If you use the same environment definition for another job, Azure Machine Learning reuses the cached image from the Workspace ACR to save time.
 
-To view the details of a cached image, check the Environments page in Azure Machine Learning studio or use the [`Environment.get_image_details`](/python/api/azureml-core/azureml.core.environment.environment#get-image-details-workspace-) method.
+To view the details of a cached image, check the Environments page in Azure Machine Learning studio or use [`MLClient.environments`](/python/api/azure-ai-ml/azure.ai.ml.mlclient#azure-ai-ml-mlclient-environments) to get and inspect the environment.
 
 To determine whether to reuse a cached image or build a new one, AzureML computes a [hash value](https://en.wikipedia.org/wiki/Hash_table) from the environment definition and compares it to the hashes of existing environments. The hash is based on the environment definition's:
 
@@ -105,10 +105,7 @@ Actual cached images in your workspace ACR will have names like `azureml/azureml
 >
 >   To update the package, specify a version number to force an image rebuild. An example of this would be changing `numpy` to `numpy==1.18.1`. New dependencies--including nested ones--will be installed, and they might break a previously working scenario.
 >
-> * Using an unpinned base image like `mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04` in your environment definition results in rebuilding the image every time the `latest` tag is updated. This helps the image receive the latest patches and system updates.
-
-> [!WARNING]
->  The [`Environment.build`](/python/api/azureml-core/azureml.core.environment.environment#build-workspace--image-build-compute-none-) method will rebuild the cached image, with the possible side-effect of updating unpinned packages and breaking reproducibility for all environment definitions corresponding to that cached image.
+> * Using an unpinned base image like `mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04` in your environment definition results in rebuilding the image every time the `latest` tag is updated. This helps the image receive the latest patches and system updates.zzs
 
 ### Image patching