Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into pauljewell-lease-blob

Author: pauljewellmsft

articles/cognitive-services/Translator/custom-translator/platform-upgrade.md

@@ -6,37 +6,37 @@ author: laujan
 manager: nitinme
 ms.service: cognitive-services
 ms.subservice: translator-text
-ms.date: 03/30/2023
+ms.date: 04/10/2023
 ms.author: lajanuar
 ms.topic: reference
 ---
 # Custom Translator platform upgrade
 
 > [!CAUTION]
 >
-> On June 02, 2023, Microsoft will retire the Custom Translator v1.0 model platform. Existing v1.0 models must migrate to the v2.0 platform for continued processing and support.
+> On June 02, 2023, Microsoft will retire the Custom Translator v1.0 model platform. Existing v1.0 models must migrate to the new platform for continued processing and support.
 
-Following measured and consistent high-quality results using models trained on the Custom Translator v2.0 platform, the v1.0 platform is retiring. Custom Translator v2.0 delivers significant improvements in many domains compared to both standard and Custom v1.0 platform translations. Migrate your v1.0 models to the v2.0 platform by June 02, 2023.
+Following measured and consistent high-quality results using models trained on the Custom Translator new platform, the v1.0 platform is retiring. The new Custom Translator platform delivers significant improvements in many domains compared to both standard and Custom v1.0 platform translations. Migrate your v1.0 models to the new platform by June 02, 2023.
 
 ## Custom Translator v1.0 upgrade timeline
 
 * **May 01, 2023** → Custom Translator v1.0 model publishing ends. There's no downtime during the v1.0 model migration. All model publishing and in-flight translation requests will continue without disruption until June 02, 2023.
 
-* **May 01, 2023 through June 02, 2023** → Customers voluntarily migrate to v2.0 models.
+* **May 01, 2023 through June 02, 2023** → Customers voluntarily migrate to new platform models.
 
 * **June 08, 2023** → Remaining v1.0 published models migrate automatically and are published by the Custom Translator team.
 
-## Upgrade to v2.0
+## Upgrade to new platform
 
 > [!IMPORTANT]
 >
 > * Starting **May 01, 2023** the upgrade wizard and workspace banner will be displayed in the Custom Translator portal indicating that you have v1.0 models to upgrade.
 > * The banner contains a **Select** button that takes you to the upgrade wizard where a list of all your v1.0 models available for upgrade are displayed.
-> * Select any or all of your v1.0 models then select **Train** to start v2.0 model upgrade training.
+> * Select any or all of your v1.0 models then select **Train** to start new platform model training.
 
 * **Check to see if you have published v1.0 models**. After signing in to the Custom Translator portal, you'll see a message indicating that you have v1.0 models to upgrade. You can also check to see if a current workspace has v1.0 models by selecting **Workspace settings** and scrolling to the bottom of the page.
 
-* **Use the upgrade wizard**. Follow the steps listed in **Upgrade to the latest version** wizard. Depending on your training data size, it may take from a few hours to a full day to upgrade your models to the v2.0 platform.
+* **Use the upgrade wizard**. Follow the steps listed in **Upgrade to the latest version** wizard. Depending on your training data size, it may take from a few hours to a full day to upgrade your models to the new platform.
 
 ## Unpublished and opt-out published models
 

articles/deployment-environments/how-to-manage-environments.md

@@ -9,7 +9,7 @@ ms.topic: how-to
 ms.date: 02/28/2023
 ---
 
-# Manage your environment
+# Manage your deployment environment
 
 In Azure Deployment Environments Preview, a development infrastructure admin gives developers access to projects and the environment types that are associated with them. After a developer has access, they can create deployment environments based on the pre-configured environment types. The permissions that the creator of the environment and the rest of team have to access the environment's resources are defined in the specific environment type.
 

articles/machine-learning/how-to-nlp-processing-batch.md

@@ -17,7 +17,7 @@ ms.custom: devplatv2
 
 [!INCLUDE [cli v2](../../includes/machine-learning-dev-v2.md)]
 
-Batch Endpoints can be used for processing tabular data, but also any other file type like text. Those deployments are supported in both MLflow and custom models. In this tutorial we will learn how to deploy a model that can perform text summarization of long sequences of text using a model from HuggingFace.
+Batch Endpoints can be used for processing tabular data that contain text. Those deployments are supported in both MLflow and custom models. In this tutorial we will learn how to deploy a model that can perform text summarization of long sequences of text using a model from HuggingFace.
 
 ## About this sample
 
@@ -27,13 +27,26 @@ The model we are going to work with was built using the popular library transfor
 * It is trained for summarization of text in English.
 * We are going to use Torch as a backend.
 
-The information in this article is based on code samples contained in the [azureml-examples](https://github.com/azure/azureml-examples) repository. To run the commands locally without having to copy/paste YAML and other files, clone the repo and then change directories to the `cli/endpoints/batch/deploy-models/huggingface-text-summarization` if you are using the Azure CLI or `sdk/python/endpoints/batch/deploy-models/huggingface-text-summarization` if you are using our SDK for Python.
+The information in this article is based on code samples contained in the [azureml-examples](https://github.com/azure/azureml-examples) repository. To run the commands locally without having to copy/paste YAML and other files, clone the repo and then change directories to the [`cli/endpoints/batch/deploy-models/huggingface-text-summarization`](https://github.com/azure/azureml-examples/tree/main/cli/endpoints/batch/deploy-models/huggingface-text-summarization) if you are using the Azure CLI or [`sdk/python/endpoints/batch/deploy-models/huggingface-text-summarization`](https://github.com/azure/azureml-examples/tree/main/sdk/python/endpoints/batch/deploy-models/huggingface-text-summarization) if you are using our SDK for Python.
+
+# [Azure CLI](#tab/cli)
 
 ```azurecli
 git clone https://github.com/Azure/azureml-examples --depth 1
 cd azureml-examples/cli/endpoints/batch/deploy-models/huggingface-text-summarization
 ```
 
+# [Python](#tab/python)
+
+In a Jupyter notebook:
+
+```python
+!git clone https://github.com/Azure/azureml-examples --depth 1
+!cd azureml-examples/sdk/python/endpoints/batch/deploy-models/huggingface-text-summarization
+```
+
+---
+
 ### Follow along in Jupyter Notebooks
 
 You can follow along this sample in a Jupyter Notebook. In the cloned repository, open the notebook: [text-summarization-batch.ipynb](https://github.com/Azure/azureml-examples/blob/main/sdk/python/endpoints/batch/deploy-models/huggingface-text-summarization/text-summarization-batch.ipynb).
@@ -46,7 +59,7 @@ You can follow along this sample in a Jupyter Notebook. In the cloned repository
 
 First, let's connect to Azure Machine Learning workspace where we're going to work on.
 
-# [Azure CLI](#tab/azure-cli)
+# [Azure CLI](#tab/cli)
 
 ```azurecli
 az account set --subscription <subscription>
@@ -80,7 +93,13 @@ ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group,
 
 ### Registering the model
 
-Due to the size of the model, it hasn't been included in this repository. Instead, you can generate a local copy with the following code. A local copy of the model will be placed at `model`. We will use it during the course of this tutorial.
+Due to the size of the model, it hasn't been included in this repository. Instead, you can download a copy from the HuggingFace model's hub. You need the packages `transformers` and `torch` installed in the environment you are using.
+
+```python
+%pip install transformers torch
+```
+
+Use the following code to download the model to a folder `model`:
 
 ```python
 from transformers import pipeline
@@ -99,7 +118,7 @@ MODEL_NAME='bart-text-summarization'
 az ml model create --name $MODEL_NAME --path "model"
 ```
 
-# [Python](#tab/sdk)
+# [Python](#tab/python)
 
 ```python
 model_name = 'bart-text-summarization'
@@ -115,7 +134,7 @@ We are going to create a batch endpoint named `text-summarization-batch` where t
 
 1. Decide on the name of the endpoint. The name of the endpoint will end-up in the URI associated with your endpoint. Because of that, __batch endpoint names need to be unique within an Azure region__. For example, there can be only one batch endpoint with the name `mybatchendpoint` in `westus2`.
 
-    # [Azure CLI](#tab/azure-cli)
+    # [Azure CLI](#tab/cli)
 
     In this case, let's place the name of the endpoint in a variable so we can easily reference it later.
 
@@ -133,7 +152,7 @@ We are going to create a batch endpoint named `text-summarization-batch` where t
 
 1. Configure your batch endpoint
 
-    # [Azure CLI](#tab/azure-cli)
+    # [Azure CLI](#tab/cli)
 
     The following YAML file defines a batch endpoint:
     
@@ -156,7 +175,7 @@ We are going to create a batch endpoint named `text-summarization-batch` where t
 
    :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/huggingface-text-summarization/deploy-and-run.sh" ID="create_batch_endpoint" :::
 
-   # [Python](#tab/sdk)
+   # [Python](#tab/python)
 
    ```python
    ml_client.batch_endpoints.begin_create_or_update(endpoint)
@@ -199,7 +218,7 @@ Let's create the deployment that will host the model:
 
    :::code language="yaml" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/huggingface-text-summarization/deployment.yml" range="7-10" :::
 
-   # [Python](#tab/sdk)
+   # [Python](#tab/python)
 
    Let's get a reference to the environment:
 
@@ -217,7 +236,7 @@ Let's create the deployment that will host the model:
    
 1. Each deployment runs on compute clusters. They support both [Azure Machine Learning Compute clusters (AmlCompute)](./how-to-create-attach-compute-cluster.md) or [Kubernetes clusters](./how-to-attach-kubernetes-anywhere.md). In this example, our model can benefit from GPU acceleration, which is why we will use a GPU cluster.
 
-   # [Azure CLI](#tab/azure-cli)
+   # [Azure CLI](#tab/cli)
 
    :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/huggingface-text-summarization/deploy-and-run.sh" ID="create_compute" :::
 
@@ -253,7 +272,7 @@ Let's create the deployment that will host the model:
 
    :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/huggingface-text-summarization/deploy-and-run.sh" ID="create_batch_deployment_set_default" :::
 
-   # [Python](#tab/sdk)
+   # [Python](#tab/python)
 
    To create a new deployment with the indicated environment and scoring script use the following code:
 
@@ -298,7 +317,7 @@ Let's create the deployment that will host the model:
    az ml batch-endpoint update --name $ENDPOINT_NAME --set defaults.deployment_name=$DEPLOYMENT_NAME
    ```
 
-   # [Python](#tab/sdk)
+   # [Python](#tab/python)
 
    ```python
    endpoint.defaults.deployment_name = deployment.name
@@ -321,7 +340,7 @@ For testing our endpoint, we are going to use a sample of the dataset [BillSum:
    > [!NOTE]
    > The utility `jq` may not be installed on every installation. You can get instructions in [this link](https://stedolan.github.io/jq/download/).
    
-   # [Python](#tab/sdk)
+   # [Python](#tab/python)
 
    ```python
    input = Input(type=AssetTypes.URI_FOLDER, path="data")
@@ -341,7 +360,7 @@ For testing our endpoint, we are going to use a sample of the dataset [BillSum:
 
    :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/huggingface-text-summarization/deploy-and-run.sh" ID="show_job_in_studio" :::
 
-   # [Python](#tab/sdk)
+   # [Python](#tab/python)
 
    ```python
    ml_client.jobs.get(job.name)
@@ -357,7 +376,7 @@ For testing our endpoint, we are going to use a sample of the dataset [BillSum:
    az ml job download --name $JOB_NAME --output-name score --download-path .
    ```
 
-   # [Python](#tab/sdk)
+   # [Python](#tab/python)
 
    ```python
    ml_client.jobs.download(name=job.name, output_name='score', download_path='./')

articles/machine-learning/samples-designer.md

@@ -12,6 +12,7 @@ ms.author: keli19
 ms.date: 10/21/2021
 ms.custom: designer
 ---
+
 # Example pipelines & datasets for Azure Machine Learning designer
 
 Use the built-in examples in Azure Machine Learning designer to quickly get started building your own machine learning pipelines. The Azure Machine Learning designer [GitHub repository](https://github.com/Azure/MachineLearningDesigner) contains detailed documentation to help you understand some common  machine learning scenarios.
@@ -116,10 +117,10 @@ The sample datasets are available under **Datasets**-**Samples** category. You c
 |-------------|:--------------------|
 | Adult Census Income Binary Classification dataset | A subset of the 1994 Census database, using working adults over the age of 16 with an adjusted income index of > 100.<br/>**Usage**: Classify people using demographics to predict whether a person earns over 50K a year.<br/> **Related Research**: Kohavi, R., Becker, B., (1996). [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml). Irvine, CA: University of California, School of Information and Computer Science|
 |Automobile price data (Raw)|Information about automobiles by make and model, including the price, features such as the number of cylinders and MPG, as well as an insurance risk score.<br/> The risk score is initially associated with auto price. It is then adjusted for actual risk in a process known to actuaries as symboling. A value of +3 indicates that the auto is risky, and a value of -3 that it is probably safe.<br/>**Usage**: Predict the risk score by features, using regression or multivariate classification.<br/>**Related Research**: Schlimmer, J.C. (1987). [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml). Irvine, CA: University of California, School of Information and Computer Science. |
-| CRM Appetency Labels Shared |Labels from the KDD Cup 2009 customer relationship prediction challenge ([orange_small_train_appetency.labels](http://www.sigkdd.org/site/2009/files/orange_small_train_appetency.labels)).|
-|CRM Churn Labels Shared|Labels from the KDD Cup 2009 customer relationship prediction challenge ([orange_small_train_churn.labels](http://www.sigkdd.org/site/2009/files/orange_small_train_churn.labels)).|
-|CRM Dataset Shared | This data comes from the KDD Cup 2009 customer relationship prediction challenge ([orange_small_train.data.zip](http://www.sigkdd.org/site/2009/files/orange_small_train.data.zip)). <br/>The dataset contains 50K customers from the French Telecom company Orange. Each customer has 230 anonymized features, 190 of which are numeric and 40 are categorical. The features are very sparse. |
-|CRM Upselling Labels Shared|Labels from the KDD Cup 2009 customer relationship prediction challenge ([orange_large_train_upselling.labels](http://www.sigkdd.org/site/2009/files/orange_large_train_upselling.labels)|
+| CRM Appetency Labels Shared |Labels from the KDD Cup 2009 customer relationship prediction challenge ([orange_small_train_appetency.labels](https://kdd.org/cupfiles/KDDCupData/2009/orange_small_train_appetency.labels)).|
+|CRM Churn Labels Shared|Labels from the KDD Cup 2009 customer relationship prediction challenge ([orange_small_train_churn.labels](https://kdd.org/cupfiles/KDDCupData/2009/files/orange_small_train_churn.labels)).|
+|CRM Dataset Shared | This data comes from the KDD Cup 2009 customer relationship prediction challenge ([orange_small_train.data.zip](https://kdd.org/cupfiles/KDDCupData/2009/orange_small_train.data.zip)). <br/>The dataset contains 50K customers from the French Telecom company Orange. Each customer has 230 anonymized features, 190 of which are numeric and 40 are categorical. The features are very sparse. |
+|CRM Upselling Labels Shared|Labels from the KDD Cup 2009 customer relationship prediction challenge ([orange_large_train_upselling.labels](https://kdd.org/cupfiles/KDDCupData/2009/orange_small_train_upselling.labels)|
 |Flight Delays Data|Passenger flight on-time performance data taken from the TranStats data collection of the U.S. Department of Transportation ([On-Time](https://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236&DB_Short_Name=On-Time)).<br/>The dataset covers the time period April-October 2013. Before uploading to the designer, the dataset was processed as follows: <br/>-    The dataset was filtered to cover only the 70 busiest airports in the continental US <br/>- Canceled flights were labeled as delayed by more than 15 minutes <br/>- Diverted flights were filtered out <br/>- The following columns were selected: Year, Month, DayofMonth, DayOfWeek, Carrier, OriginAirportID, DestAirportID, CRSDepTime, DepDelay, DepDel15, CRSArrTime, ArrDelay, ArrDel15, Canceled|
 |German Credit Card UCI dataset|The UCI Statlog (German Credit Card) dataset ([Statlog+German+Credit+Data](https://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data))), using the german.data file.<br/>The dataset classifies people, described by a set of attributes, as low or high credit risks. Each example represents a person. There are 20 features, both numerical and categorical, and a binary label (the credit risk value). High credit risk entries have label = 2, low credit risk entries have label = 1. The cost of misclassifying a low risk example as high is 1, whereas the cost of misclassifying a high risk example as low is 5.|
 |IMDB Movie Titles|The dataset contains information about movies that were rated in Twitter tweets: IMDB movie ID, movie name, genre, and production year. There are 17K movies in the dataset. The dataset was introduced in the paper "S. Dooms, T. De Pessemier and L. Martens. MovieTweetings: a Movie Rating Dataset Collected From Twitter. Workshop on Crowdsourcing and Human Computation for Recommender Systems, CrowdRec at RecSys 2013."|
@@ -137,3 +138,4 @@ The sample datasets are available under **Datasets**-**Samples** category. You c
 ## Next steps
 
 Learn the fundamentals of predictive analytics and machine learning with [Tutorial: Predict automobile price with the designer](tutorial-designer-automobile-price-train-score.md)
+

articles/storage/blobs/create-data-lake-storage-account.md

@@ -2,10 +2,10 @@
 title: Create a storage account for Azure Data Lake Storage Gen2
 titleSuffix: Azure Storage
 description: Learn how to create a storage account for use with Azure Data Lake Storage Gen2.
-author: jimmart-dev
+author: normesta
 
 ms.topic: how-to
-ms.author: jammart
+ms.author: normesta
 ms.date: 03/09/2023
 ms.service: storage
 ms.subservice: data-lake-storage-gen2

articles/storage/blobs/data-lake-storage-access-control-model.md

@@ -2,13 +2,13 @@
 title: Access control model for Azure Data Lake Storage Gen2
 titleSuffix: Azure Storage
 description: Learn how to configure container, directory, and file-level access in accounts that have a hierarchical namespace.
-author: jimmart-dev
+author: normesta
 
 ms.subservice: data-lake-storage-gen2
 ms.service: storage
 ms.topic: conceptual
 ms.date: 03/09/2023
-ms.author: jammart
+ms.author: normesta
 ms.custom: engagement-fy23
 ---
 

articles/storage/blobs/data-lake-storage-access-control.md

@@ -2,13 +2,13 @@
 title: Access control lists in Azure Data Lake Storage Gen2
 titleSuffix: Azure Storage
 description: Understand how POSIX-like ACLs access control lists work in Azure Data Lake Storage Gen2.
-author: jimmart-dev
+author: normesta
 
 ms.subservice: data-lake-storage-gen2
 ms.service: storage
 ms.topic: conceptual
 ms.date: 03/09/2023
-ms.author: jammart
+ms.author: normesta
 ms.reviewer: jamesbak
 ms.devlang: python
 ms.custom: engagement-fy23

articles/storage/blobs/data-lake-storage-acl-azure-portal.md

@@ -2,13 +2,13 @@
 title: Use the Azure portal to manage ACLs in Azure Data Lake Storage Gen2
 titleSuffix: Azure Storage
 description: Use the Azure portal to manage access control lists (ACLs) in storage accounts that have a hierarchical namespace (HNS) enabled.
-author: jimmart-dev
+author: normesta
 
 ms.subservice: data-lake-storage-gen2
 ms.service: storage
 ms.topic: how-to
 ms.date: 03/09/2023
-ms.author: jammart
+ms.author: normesta
 ---
 
 # Use the Azure portal to manage ACLs in Azure Data Lake Storage Gen2