Update how-to-manage-environments-v2.md

Author: ositanachi

articles/machine-learning/how-to-manage-environments-v2.md

@@ -136,6 +136,9 @@ ml_client.environments.create_or_update(env_docker_image)
 
 Instead of defining an environment from a prebuilt image, you can also define one from a Docker [build context](https://docs.docker.com/develop/develop-images/dockerfile_best-practices/#understand-build-context). To do so, specify the directory that will serve as the build context. This directory should contain a Dockerfile and any other files needed to build the image.
 
+> [!NOTE]
+> Uploading a Dockerfile larger than 1MB is currently unsupported.
+
 # [Azure CLI](#tab/cli)
 
 The following example is a YAML specification file for an environment defined from a build context. The local path to the build context folder is specified in the `build.path` field, and the relative path to the Dockerfile within that build context folder is specified in the `build.dockerfile_path` field. If `build.dockerfile_path` is omitted in the YAML file, Azure Machine Learning will look for a Dockerfile named `Dockerfile` at the root of the build context.
@@ -167,9 +170,6 @@ ml_client.environments.create_or_update(env_docker_context)
 
 Azure Machine Learning will start building the image from the build context when the environment is created. You can monitor the status of the build and view the build logs in the studio UI.
 
-> [!NOTE]
-> Uploading a Dockerfile larger than 1MB is currently unsupported.
-
 ### Create an environment from a conda specification
 
 You can define an environment using a standard conda YAML configuration file that includes the dependencies for the conda environment. See [Creating an environment manually](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-file-manually) for information on this standard format.