Merge pull request #89402 from vmagelo/azureml-data-work

Changes inspired by work on azureml.data sdk

Author: PRMerger20

articles/machine-learning/service/how-to-access-data.md

@@ -18,7 +18,7 @@ ms.custom: seodec18
 
 # Access data in Azure storage services
 
- In this article, learn how to easily access your data in Azure storage services via Azure Machine Learning datastores. Datastores are used to store connection information, like your subscription ID and token authorization,  to access your storage without having to hard code that information in your scripts.
+In this article, learn how to easily access your data in Azure storage services via Azure Machine Learning datastores. Datastores are used to store connection information, like your subscription ID and token authorization. Using datastores allows you to access your storage without having to hard code connection information in your scripts.
 
 This how-to shows examples of the following tasks:
 * [Register datastores](#access)
@@ -47,7 +47,7 @@ All the register methods are on the [`Datastore`](https://docs.microsoft.com/pyt
 
 The following examples show you to register an Azure Blob Container or an Azure File Share as a datastore.
 
-+ For an **Azure Blob Container Datastore**, use [`register_azure_blob-container()`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.datastore(class)?view=azure-ml-py)
++ For an **Azure Blob Container Datastore**, use [`register_azure_blob-container()`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.datastore(class)?view=azure-ml-py#register-azure-blob-container-workspace--datastore-name--container-name--account-name--sas-token-none--account-key-none--protocol-none--endpoint-none--overwrite-false--create-if-not-exists-false--skip-validation-false--blob-cache-timeout-none--grant-workspace-access-false--subscription-id-none--resource-group-none-)
 
   ```Python
   datastore = Datastore.register_azure_blob_container(workspace=ws, 
@@ -82,7 +82,7 @@ To get a specific datastore registered in the current workspace, use the [`get()
 #get named datastore from current workspace
 datastore = Datastore.get(ws, datastore_name='your datastore name')
 ```
-To get the list of datastores registered with a given workspace, you can use the `datastores` property on a workspace object:
+To get the list of datastores registered with a given workspace, you can use the [`datastores`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace%28class%29?view=azure-ml-py#datastores) property on a workspace object:
 
 ```Python
 #list all datastores registered in current workspace
@@ -108,7 +108,7 @@ ws.set_default_datastore('your datastore name')
 
 <a name="up-and-down"></a>
 ## Upload & download data
-The [`upload()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.azure_storage_datastore.azureblobdatastore?view=azure-ml-py#download-target-path--prefix-none--overwrite-false--show-progress-true-) and [`download()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.azure_storage_datastore.azureblobdatastore?view=azure-ml-py#download-target-path--prefix-none--overwrite-false--show-progress-true-) methods described in the following examples are specific to and operate identically for the [AzureBlobDatastore](https://docs.microsoft.com/python/api/azureml-core/azureml.data.azure_storage_datastore.azureblobdatastore?view=azure-ml-py) and [AzureFileDatastore](https://docs.microsoft.com/python/api/azureml-core/azureml.data.azure_storage_datastore.azurefiledatastore?view=azure-ml-py) classes.
+The [`upload()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.azure_storage_datastore.azureblobdatastore?view=azure-ml-py#upload-src-dir--target-path-none--overwrite-false--show-progress-true-) and [`download()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.azure_storage_datastore.azureblobdatastore?view=azure-ml-py#download-target-path--prefix-none--overwrite-false--show-progress-true-) methods described in the following examples are specific to and operate identically for the [AzureBlobDatastore](https://docs.microsoft.com/python/api/azureml-core/azureml.data.azure_storage_datastore.azureblobdatastore?view=azure-ml-py) and [AzureFileDatastore](https://docs.microsoft.com/python/api/azureml-core/azureml.data.azure_storage_datastore.azurefiledatastore?view=azure-ml-py) classes.
 
 ### Upload
 
@@ -153,8 +153,8 @@ The following table lists the methods that tell the compute target how to use th
 Way|Method|Description|
 ----|-----|--------
 Mount| [`as_mount()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.azure_storage_datastore.abstractazurestoragedatastore?view=azure-ml-py#as-mount--)| Use to mount the datastore on the compute target.
-Download|[`as_download()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.azure_storage_datastore.abstractazurestoragedatastore?view=azure-ml-py#as-download-path-on-compute-none-)|Use to download the contents of your datastore to the location specified by `path_on_compute`. <br> This download happens before the run.
-Upload|[`as_upload()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.azure_storage_datastore.abstractazurestoragedatastore?view=azure-ml-py#as-upload-path-on-compute-none-)| Use to upload a file from the location specified by `path_on_compute` to your datastore. <br> This upload happens after your run.
+Download|[`as_download()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.azure_storage_datastore.abstractazurestoragedatastore?view=azure-ml-py#as-download-path-on-compute-none-)|Use to download the contents of your datastore to the location specified by `path_on_compute`. <br><br> This download happens before the run.
+Upload|[`as_upload()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.azure_storage_datastore.abstractazurestoragedatastore?view=azure-ml-py#as-upload-path-on-compute-none-)| Use to upload a file from the location specified by `path_on_compute` to your datastore. <br><br> This upload happens after your run.
 
 To reference a specific folder or file in your datastore and make it available on the compute target, use the datastore [`path()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.azure_storage_datastore.abstractazurestoragedatastore?view=azure-ml-py#path-path-none--data-reference-name-none-) method.
 
@@ -166,13 +166,13 @@ datastore.as_mount()
 datastore.path('./bar').as_download()
 ```
 > [!NOTE]
-> Any `datastore` or `datastore.path` object resolves to an environment variable name of the format `"$AZUREML_DATAREFERENCE_XXXX"`, whose value represents the mount/download path on the target compute. The datastore path on the target compute might not be the same as the execution path for the training script.
+> Any specified `datastore` or `datastore.path` object resolves to an environment variable name of the format `"$AZUREML_DATAREFERENCE_XXXX"`, whose value represents the mount/download path on the target compute. The datastore path on the target compute might not be the same as the execution path for the training script.
 
 ### Examples 
 
 The following code examples are specific to the [`Estimator`](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.estimator.estimator?view=azure-ml-py) class for accessing data during training. 
 
-`script_params` is a dictionary containing parameters to the entry_script. You can use it to pass in a datastore and describe how data should be made available on compute target. Learn more from our end-to-end [tutorial](tutorial-train-models-with-aml.md).
+`script_params` is a dictionary containing parameters to the entry_script. Use it to pass in a datastore and describe how data is made available on the compute target. Learn more from our end-to-end [tutorial](tutorial-train-models-with-aml.md).
 
 ```Python
 from azureml.train.estimator import Estimator
@@ -239,7 +239,7 @@ Azure Machine Learning provides several ways to use your models for scoring. Som
 | [Web service](how-to-deploy-and-where.md) | &nbsp; | Deploy model(s) as a web service. |
 | [IoT Edge module](how-to-deploy-and-where.md) | &nbsp; | Deploy model(s) to IoT Edge devices. |
 
-For situations where the SDK does not provide access to datastores, you may be able to create custom code using the relevant Azure SDK to access the data. For example, using the [Azure Storage SDK for Python](https://github.com/Azure/azure-storage-python) to access data stored in blobs.
+For situations where the SDK doesn't provide access to datastores, you may be able to create custom code using the relevant Azure SDK to access the data. For example, the [Azure Storage SDK for Python](https://github.com/Azure/azure-storage-python) is a client library that you can use to access data stored in blobs or files.
 
 
 ## Next steps

articles/machine-learning/service/how-to-create-register-datasets.md

@@ -168,9 +168,9 @@ mnist_ds = Dataset.File.from_files(path=web_paths)
 
 ## Register datasets
 
-To complete the creation process, register your datasets with workspace:
+To complete the creation process, register your datasets with a workspace.
 
-Use the [`register()`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#register-workspace--name--description-none--tags-none--visible-true--exist-ok-false--update-if-exist-false-) method to register datasets to your workspace so they can be shared with others and reused across various experiments.
+Use the [`register()`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#register-workspace--name--description-none--tags-none--visible-true--exist-ok-false--update-if-exist-false-) method to register datasets with your workspace so they can be shared with others and reused across various experiments.
 
 ```Python
 titanic_ds = titanic_ds.register(workspace = workspace,
@@ -183,7 +183,8 @@ titanic_ds = titanic_ds.register(workspace = workspace,
 
 ## Version datasets
 
-You can register a new dataset under the same name by creating a new version. Dataset version is a way to bookmark the state of your data, so you can apply a specific version of the dataset for experimentation or future reproduction. Typical scenarios to consider versioning:
+You can register a new dataset under the same name by creating a new version. Dataset version is a way to bookmark the state of your data, so you can apply a specific version of the dataset for experimentation or future reproduction. Typical scenarios to consider for versioning are: 
+
 * When new data is available for retraining.
 * When you are applying different data preparation or feature engineering approaches.