Merge pull request #234909 from fbsolo-ms1/updates-for-AMAR

American-Dipper · web-flow · commit 0c514378b161 · 2023-04-18T16:21:10.000-07:00
File and TOC updates.
diff --git a/articles/machine-learning/how-to-connection.md b/articles/machine-learning/how-to-connection.md
@@ -1,5 +1,5 @@
 ---
-title: Use connections (preview)
+title: Create connections to external data sources (preview)
 titleSuffix: Azure Machine Learning
 description: Learn how to use connections to connect to External data sources for training with Azure Machine Learning.
 services: machine-learning
@@ -9,7 +9,7 @@ ms.topic: how-to
 ms.author: ambadal
 author: AmarBadal
 ms.reviewer: franksolomon
-ms.date: 04/11/2023
+ms.date: 04/18/2023
 ms.custom: data4ml
 
 # Customer intent: As an experienced data scientist with Python skills, I have data located in external sources outside of Azure. I need to make that data available to the Azure Machine Learning platform, to train my machine learning models.
@@ -34,9 +34,34 @@ In this article, learn how to connect to data sources located outside of Azure,
 
 - An Azure Machine Learning workspace.
 
-> [!NOTE]
+> [!IMPORTANT]
 > An Azure Machine Learning connection securely stores the credentials passed during connection creation in the Workspace Azure Key Vault. A connection references the credentials from the key vault storage location for further use. You won't need to directly deal with the credentials after they are stored in the key vault. You have the option to store the credentials in the YAML file. A CLI command or SDK can override them. We recommend that you **avoid** credential storage in a YAML file, because a security breach could lead to a credential leak.
 
+> [!NOTE]
+> For a successful data import, please verify that you have installed the latest azure-ai-ml package (version 1.5.0 or later) for SDK, and the ml extension (version 2.15.1 or later).  
+> 
+> If you have an older SDK package or CLI extension, please remove the old one and install the new one with the code shown in the tab section. Follow the instructions for SDK and CLI below:
+
+### Code versions
+
+# [SDK](#tab/SDK)
+
+```python
+pip uninstall azure-ai-ml
+pip install azure-ai-ml
+pip show azure-ai-ml #(the version value needs to be 1.5.0 or later)
+```
+
+# [CLI](#tab/CLI)
+
+```cli
+az extension remove -n ml
+az extension add -n ml --yes
+az extension show -n ml #(the version value needs to be 2.15.1 or later)
+```
+
+---
+
 ## Create a Snowflake DB connection
 
 # [CLI: Username/password](#tab/cli-username-password)
@@ -95,8 +120,9 @@ from azure.ai.ml.entities import UsernamePasswordConfiguration
 
 target= "jdbc:snowflake://<myaccount>.snowflakecomputing.com/?db=<mydb>&warehouse=<mywarehouse>&role=<myrole>"
 # add the Snowflake account, database, warehouse name and role name here. If no role name provided it will default to PUBLIC
-
-wps_connection = WorkspaceConnection(type="snowflake",
+name= <my_snowflake_connection> # name of the connection
+wps_connection = WorkspaceConnection(name= name,
+type="snowflake",
 target= target,
 credentials= UsernamePasswordConfiguration(username="XXXXX", password="XXXXXX")
 )
@@ -168,7 +194,9 @@ from azure.ai.ml.entities import UsernamePasswordConfiguration
 target= "Server=tcp:<myservername>,<port>;Database=<mydatabase>;Trusted_Connection=False;Encrypt=True;Connection Timeout=30"
 # add the sql servername, port addresss and database
 
-wps_connection = WorkspaceConnection(type="azure_sql_db",
+name= <my_sql_connection> # name of the connection
+wps_connection = WorkspaceConnection(name= name,
+type="azure_sql_db",
 target= target,
 credentials= UsernamePasswordConfiguration(username="XXXXX", password="XXXXXX")
 )
@@ -228,7 +256,9 @@ from azure.ai.ml.entities import WorkspaceConnection
 from azure.ai.ml.entities import AccessKeyConfiguration
 
 target = "https://<mybucket>.amazonaws.com" # add the s3 bucket details
-wps_connection = WorkspaceConnection(type="s3",
+name=<my_s3_connection> # name of the connection
+wps_connection = WorkspaceConnection(name=name,
+type="s3",
 target= target,
 credentials= AccessKeyConfiguration(access_key_id="XXXXXX",acsecret_access_key="XXXXXXXX")
 )
diff --git a/articles/machine-learning/how-to-import-data-assets.md b/articles/machine-learning/how-to-import-data-assets.md
@@ -1,15 +1,15 @@
 ---
-title: Import Data (preview)
+title: Import data (preview)
 titleSuffix: Azure Machine Learning
-description: Learn how to import data from external sources on to Azure Machine Learning platform
+description: Learn how to import data from external sources to the Azure Machine Learning platform.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: mldata
 ms.topic: how-to
 ms.author: ambadal
 author: AmarBadal
 ms.reviewer: franksolomon
-ms.date: 04/12/2023
+ms.date: 04/18/2023
 ms.custom: data4ml
 ---
 
@@ -43,9 +43,34 @@ To create and work with data assets, you need:
 
 * [Workspace connections created](how-to-connection.md)
 
-## Importing from external database sources / import from external sources to create a mltable data asset
+> [!NOTE]
+> For a successful data import, please verify that you have installed the latest azure-ai-ml package (version 1.5.0 or later) for SDK, and the ml extension (version 2.15.1 or later).  
+> 
+> If you have an older SDK package or CLI extension, please remove the old one and install the new one with the code shown in the tab section. Follow the instructions for SDK and CLI below:
+
+### Code versions
+
+# [SDK](#tab/SDK)
 
-> [!NOTE] 
+```python
+pip uninstall azure-ai-ml
+pip install azure-ai-ml
+pip show azure-ai-ml #(the version value needs to be 1.5.0 or later)
+```
+
+# [CLI](#tab/CLI)
+
+```cli
+az extension remove -n ml
+az extension add -n ml --yes
+az extension show -n ml #(the version value needs to be 2.15.1 or later)
+```
+
+---
+
+## Importing from an external database source as a table data asset
+
+> [!NOTE]
 > The external databases can have Snowflake, Azure SQL, etc. formats.
 
 The following code samples can import data from external databases. The `connection` that handles the import action determines the external database data source metadata. In this sample, the code imports data from a Snowflake resource. The connection points to a Snowflake source. With a little modification, the connection can point to an Azure SQL database source and an Azure SQL database source. The imported asset `type` from an external database source is `mltable`.
@@ -87,7 +112,7 @@ from azure.ai.ml import MLClient
 # Supported connections include:
 # Connection: azureml:<workspace_connection_name>
 # Supported paths include:
-# Datastore: azureml://datastores/<data_store_name>/paths/<my_path>/${{name}}
+# path: azureml://datastores/<data_store_name>/paths/<my_path>/${{name}}
 
 ml_client = MLClient.from_config()
 
@@ -102,7 +127,7 @@ ml_client.data.import_data(data_import=data_import)
 
 ---
 
-## Import data from external data and file system resources to create a uri_folder data asset
+## Import data from an external file system source as a folder data asset
 
 > [!NOTE]
 > An Amazon S3 data resource can serve as an external file system resource.
@@ -120,7 +145,7 @@ $schema: http://azureml/sdk-2-0/DataImport.json
 # Supported connections include:
 # Connection: azureml:<workspace_connection_name>
 # Supported paths include:
-# Datastore: azureml://datastores/<data_store_name>/paths/<my_path>/${{name}}
+# path: azureml://datastores/<data_store_name>/paths/<my_path>/${{name}}
 
 
 type: uri_folder
@@ -148,7 +173,7 @@ from azure.ai.ml import MLClient
 # Supported connections include:
 # Connection: azureml:<workspace_connection_name>
 # Supported paths include:
-# Datastore: azureml://datastores/<data_store_name>/paths/<my_path>/${{name}}
+# path: azureml://datastores/<data_store_name>/paths/<my_path>/${{name}}
 
 ml_client = MLClient.from_config()
 
@@ -194,4 +219,4 @@ ml_client.data.show_materialization_status(name="<name>")
 
 - [Read data in a job](how-to-read-write-data-v2.md#read-data-in-a-job)
 - [Working with tables in Azure Machine Learning](how-to-mltable.md)
-- [Access data from Azure cloud storage during interactive development](how-to-access-data-interactive.md)
+- [Access data from Azure cloud storage during interactive development](how-to-access-data-interactive.md)
diff --git a/articles/machine-learning/toc.yml b/articles/machine-learning/toc.yml
@@ -594,6 +594,14 @@
         - name: Create data assets
           displayName: Create data assets
           href: how-to-create-data-assets.md
+        - name: Create data assets by importing data from external sources
+          items:
+            - name: Create connections to external data sources (preview)
+              displayName: Create connections to external data sources (preview)
+              href: how-to-connection.md
+            - name: Import data (preview)
+              displayName: Import data (preview)
+              href: how-to-import-data-assets.md
         - name: Access and explore your data
           items:
             - name: Access data during interactive development
@@ -602,12 +610,6 @@
             - name: Access data in jobs
               displayName: Access data in jobs
               href: how-to-read-write-data-v2.md
-            - name: Import data
-              displayName: Import data
-              href: how-to-import-data-assets.md
-            - name: Use connections
-              displayName: Use connections
-              href: how-to-connection.md
             - name: Working with Tables (mltable)
               displayName: Working with Tables (mltable)
               href: how-to-mltable.md
@@ -1086,8 +1088,6 @@
               href: how-to-use-pipeline-ui.md
             - name: How to use parallel job in pipeline
               href: how-to-use-parallel-job-in-pipeline.md
-            - name: How to use pipeline component in pipeline
-              href: how-to-use-pipeline-component.md
             # v1
             - name: Create ML pipelines (Python)
               href: ./v1/how-to-create-machine-learning-pipelines.md
@@ -1235,8 +1235,6 @@
           href: reference-yaml-model.md
         - name: Schedule
           href: reference-yaml-schedule.md
-        - name: Pipeline component
-          href: reference-yaml-component-pipeline.md
         - name: Compute
           items:
             - name: Compute cluster (AmlCompute)
