fixing links to unblock contributor

Author: Blackmist

articles/machine-learning/concept-azure-machine-learning-v2.md

@@ -272,13 +272,13 @@ In custom environments, you're responsible for setting up your environment and i
 
 ### [Python SDK](#tab/sdk)
 
-To create an environment using Python SDK v2, see [Create an environment](how-to-manage-environments-v2.md?tabs=python#create-an-environment).
+To create an environment using Python SDK v2, see [Create an environment](how-to-manage-environments-v2.md?tabs=python#create-a-custom-environment).
 
 This [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/assets/environment/environment.ipynb) shows more ways to create custom environments using SDK v2.
 
 ### [Azure CLI](#tab/cli)
 
-To create an environment using CLI v2, see [Create an environment](how-to-manage-environments-v2.md?tabs=cli#create-an-environment).
+To create an environment using CLI v2, see [Create an environment](how-to-manage-environments-v2.md?tabs=cli#create-a-custom-environment).
 
 For more information, see [environment YAML schema](reference-yaml-environment.md).
 

articles/machine-learning/concept-environments.md

@@ -42,7 +42,7 @@ You use system-managed environments when you want [conda](https://conda.io/docs/
 
 You can create environments from clients like the Azure Machine Learning Python SDK, Azure Machine Learning CLI, Environments page in Azure Machine Learning studio, and [VS Code extension](how-to-manage-resources-vscode.md#create-environment). Every client allows you to customize the base image, Dockerfile, and Python layer if needed.
 
-For specific code samples, see the "Create an environment" section of [How to use environments](how-to-manage-environments-v2.md#create-an-environment). 
+For specific code samples, see the "Create an environment" section of [How to use environments](how-to-manage-environments-v2.md#create-a-custom-environment). 
 
 Environments are also easily managed through your workspace, which allows you to:
 

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

@@ -408,15 +408,15 @@ In this example, we specify the `path` (where to upload files from) inline. The
 
 For registration, you can extract the YAML definitions of `model` and `environment` into separate YAML files and use the commands `az ml model create` and `az ml environment create`. To learn more about these commands, run `az ml model create -h` and `az ml environment create -h`.
 
-For more information on registering your model as an asset, see [Register your model as an asset in Machine Learning by using the CLI](how-to-manage-models.md#register-your-model-as-an-asset-in-machine-learning-by-using-the-cli). For more information on creating an environment, see [Manage Azure Machine Learning environments with the CLI & SDK (v2)](how-to-manage-environments-v2.md#create-an-environment).
+For more information on registering your model as an asset, see [Register your model as an asset in Machine Learning by using the CLI](how-to-manage-models.md#register-your-model-as-an-asset-in-machine-learning-by-using-the-cli). For more information on creating an environment, see [Manage Azure Machine Learning environments with the CLI & SDK (v2)](how-to-manage-environments-v2.md#create-a-custom-environment).
 
 # [Python](#tab/python)
 
 In this example, we specify the `path` (where to upload files from) inline. The SDK automatically uploads the files and registers the model and environment. As a best practice for production, you should register the model and environment and specify the registered name and version separately in the codes.
 
 For more information on registering your model as an asset, see [Register your model as an asset in Machine Learning by using the SDK](how-to-manage-models.md#register-your-model-as-an-asset-in-machine-learning-by-using-the-sdk).
 
-For more information on creating an environment, see [Manage Azure Machine Learning environments with the CLI & SDK (v2)](how-to-manage-environments-v2.md#create-an-environment).
+For more information on creating an environment, see [Manage Azure Machine Learning environments with the CLI & SDK (v2)](how-to-manage-environments-v2.md#create-a-custom-environment).
 
 # [Studio](#tab/azure-studio)
 
@@ -441,7 +441,7 @@ To register the example model, follow these steps:
 
 For more information on working with registered models, see [Register and work with models](how-to-manage-models.md).
 
-For information on creating an environment in the studio, see [Create an environment](how-to-manage-environments-in-studio.md#create-an-environment).
+For information on creating an environment in the studio, see [Create an environment](how-to-manage-environments-in-studio.md#create-a-custom-environment).
 
 # [ARM template](#tab/arm)
 

articles/machine-learning/how-to-safely-rollout-online-endpoints.md

@@ -252,7 +252,7 @@ In the `blue-deployment.yaml` file, we specify the `path` (where to upload files
 
 For registration, you can extract the YAML definitions of `model` and `environment` into separate YAML files and use the commands `az ml model create` and `az ml environment create`. To learn more about these commands, run `az ml model create -h` and `az ml environment create -h`.
 
-For more information on registering your model as an asset, see [Register your model as an asset in Machine Learning by using the CLI](how-to-manage-models.md#register-your-model-as-an-asset-in-machine-learning-by-using-the-cli). For more information on creating an environment, see [Manage Azure Machine Learning environments with the CLI & SDK (v2)](how-to-manage-environments-v2.md#create-an-environment).
+For more information on registering your model as an asset, see [Register your model as an asset in Machine Learning by using the CLI](how-to-manage-models.md#register-your-model-as-an-asset-in-machine-learning-by-using-the-cli). For more information on creating an environment, see [Manage Azure Machine Learning environments with the CLI & SDK (v2)](how-to-manage-environments-v2.md#create-a-custom-environment).
 
 # [Python](#tab/python)
 
@@ -284,7 +284,7 @@ The following table describes the attributes of a `deployment`:
 
     For more information on registering your model as an asset, see [Register your model as an asset in Machine Learning by using the SDK](how-to-manage-models.md#register-your-model-as-an-asset-in-machine-learning-by-using-the-sdk).
 
-    For more information on creating an environment, see [Manage Azure Machine Learning environments with the CLI & SDK (v2)](how-to-manage-environments-v2.md#create-an-environment).
+    For more information on creating an environment, see [Manage Azure Machine Learning environments with the CLI & SDK (v2)](how-to-manage-environments-v2.md#create-a-custom-environment).
 
     > [!NOTE]
     > To create a deployment for a Kubernetes online endpoint, use the `KubernetesOnlineDeployment` class.
@@ -321,7 +321,7 @@ To register the example model, follow these steps:
 
 For more information on working with registered models, see [Register and work with models](how-to-manage-models.md).
 
-For information on creating an environment in the studio, see [Create an environment](how-to-manage-environments-in-studio.md#create-an-environment).
+For information on creating an environment in the studio, see [Create an environment](how-to-manage-environments-in-studio.md#create-a-custom-environment).
 
 ### Create a managed online endpoint and the 'blue' deployment
 

articles/machine-learning/how-to-train-distributed-gpu.md

@@ -41,7 +41,7 @@ Azure Machine Learning offers an [MPI job](https://www.mcs.anl.gov/research/proj
 
 To run distributed training using MPI, follow these steps:
 
-1. Use an Azure Machine Learning environment with the preferred deep learning framework and MPI. Azure Machine Learning provides [curated environments](resource-curated-environments.md) for popular frameworks. Or [create a custom environment](how-to-manage-environments-v2.md#create-an-environment) with the preferred deep learning framework and MPI.
+1. Use an Azure Machine Learning environment with the preferred deep learning framework and MPI. Azure Machine Learning provides [curated environments](resource-curated-environments.md) for popular frameworks. Or [create a custom environment](how-to-manage-environments-v2.md#create-a-custom-environment) with the preferred deep learning framework and MPI.
 1. Define  a `command` with `instance_count`. `instance_count` should be equal to the number of GPUs per node for per-process-launch, or set to 1 (the default) for per-node-launch if the user script is responsible for launching the processes per node.
 1. Use the `distribution` parameter of the `command` to specify settings for `MpiDistribution`.