update

Author: cherylmc

articles/active-directory/roles/permissions-reference.md

@@ -4,7 +4,7 @@ titleSuffix: Azure Digital Twins
 description: Migrate from preview API versions of the control plane to the stable GA version
 author: baanders
 ms.author: baanders
-ms.date: 01/26/2023
+ms.date: 02/08/2023
 ms.topic: conceptual
 ms.service: digital-twins
 services: digital-twins
@@ -38,13 +38,13 @@ If you're using an associated SDK of the 2020-03-01-preview API, it's recommende
 * Go: Migrate from the [digitaltwins 2020-03-01-preview](https://pkg.go.dev/github.com/Azure/[email protected]+incompatible/services/preview/digitaltwins/mgmt/2020-03-01-preview/digitaltwins) package to the [armsdigitaltwins v1.0.0](https://pkg.go.dev/github.com/Azure/azure-sdk-for-go/sdk/resourcemanager/digitaltwins/armdigitaltwins) package, by following the [Go SDK migration guidance](https://github.com/Azure/azure-sdk-for-go/blob/main/documentation/MIGRATION_GUIDE.md).
 
 
-## Migrate from 2021-10-31-preview control plane API 
+## Migrate from 2021-06-30-preview control plane API 
 
 The target replacement API version ([2022-10-31 (GA) API version](https://github.com/Azure/azure-rest-api-specs/tree/main/specification/digitaltwins/resource-manager/Microsoft.DigitalTwins/stable/2022-10-31)) is backwards-compatible with the [2021-06-30-preview API version](https://github.com/Azure/azure-rest-api-specs/tree/main/specification/digitaltwins/resource-manager/Microsoft.DigitalTwins/preview/2021-06-30-preview). In addition to the 2021-06-30-preview API version's capabilities, the 2022-10-31 (GA) API version additionally introduces user-assigned managed identities. 
 
 There are no breaking changes for migrating to the target replacement API version ([2022-10-31 (GA) API version](https://github.com/Azure/azure-rest-api-specs/tree/main/specification/digitaltwins/resource-manager/Microsoft.DigitalTwins/stable/2022-10-31)). There's one minor change: Endpoints and TSDB connections now have an explicit *Updating* state defined in the API contract. 
 
-If you're using an associated SDK of the 2021-10-31-preview API, it's recommended to update to the latest version of the management SDK. Use the following links to know what additional changes are required to migrate to the new management SDK.
+If you're using an associated SDK of the 2021-06-30-preview API, it's recommended to update to the latest version of the management SDK. Use the following links to know what additional changes are required to migrate to the new management SDK.
 * .NET: Migrate from the [Microsoft.Azure.Management.DigitalTwins 1.2.0-beta.1](https://www.nuget.org/packages/Microsoft.Azure.Management.DigitalTwins/1.2.0-beta.1) package to the [Azure.ResourceManager.DigitalTwins 1.1.0](https://www.nuget.org/packages/Azure.ResourceManager.DigitalTwins/1.1.0) package, by following the [.NET SDK migration guidance](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/resourcemanager/Azure.ResourceManager/docs/MigrationGuide.md). 
 * Java: Migrate from the [azure-resourcemanager-digitaltwins:1.0.0-beta.2](https://search.maven.org/artifact/com.azure.resourcemanager/azure-resourcemanager-digitaltwins/1.0.0-beta.2/jar) package to the [Azure.ResourceManager.DigitalTwins 1.1.0](https://search.maven.org/artifact/com.azure.resourcemanager/azure-resourcemanager-digitaltwins/1.1.0/jar) package, or set the service version in the SDK client to call the 2022-10-31 (GA) API.
 

articles/digital-twins/resources-migrate-from-preview-apis.md

@@ -9,31 +9,26 @@ ms.date: 06/03/2022
 ms.author: kesheth
 ---
 
-# Validate FHIR resources against profiles in Azure API for FHIR
 
-`$validate` is an operation in Fast Healthcare Interoperability Resources (FHIR®) that allows you to ensure that a FHIR resource conforms to the base resource requirements or a specified profile. This is a valuable operation to ensure that the data in Azure API for FHIR  has the expected attributes and values.
+# Validate Operation : Overview
 
-In the [store profiles in Azure API for FHIR](store-profiles-in-fhir.md) article, you walked through the basics of FHIR profiles and storing them. This article will guide you through how to use `$validate` for validating resources against profiles. For more information about FHIR profiles outside of this article, visit 
-[HL7.org](https://www.hl7.org/fhir/profiling.html).
+In the [store profiles in Azure API for FHIR](store-profiles-in-fhir.md) article, you walked through the basics of FHIR profiles and storing them. This article will guide you through how to use `$validate` for validating resources against profiles. Validating a resource against a profile means checking if the resource conforms to the profile, including the specifications listed in `Resource.meta.profile` or in an Implementation Guide.
 
+`$validate` is an operation in Fast Healthcare Interoperability Resources (FHIR®) that allows you to ensure that a FHIR resource conforms to the base resource requirements or a specified profile. This operation ensures that the data in Azure API for FHIR  has the expected attributes and values. For information on validate operation, visit [HL7 FHIR Specification](https://www.hl7.org/fhir/resource-operation-validate.html). Per specification, Mode can be specified with `$validate`, such as create and update:
 
-## Validating resources against the profiles
-
-FHIR resources can express their conformance to specific profiles. This allows Azure API for FHIR to **validate** given resources against profiles. Validating a resource against a profile means checking if the resource conforms to the profile, including the specifications listed in `Resource.meta.profile` or in an Implementation Guide. There are two ways for you to validate your resource:
-
-- You can use `$validate` operation against a resource that is already in Azure API for FHIR. 
-- You can include `$validate` when you create or update a resource. 
+- `create`: Azure API for FHIR checks that the profile content is unique from the existing resources and that it's acceptable to be created as a new resource.
+- `update`: Checks that the profile is an update against the nominated existing resource (that is no changes are made to the immutable fields).
 
-In both cases, you can decide what to do if the Azure API for FHIR configuration resource doesn't conform to your desired profile.
+There are different ways provided for you to validate resource:
 
-## Using $validate
+- Validate an existing resource with validate operation.
+- Validate a new resource with validate operation.
+- Validate on resource CREATE/ UPDATE using header.
 
-The `$validate` operation checks whether the provided profile is valid, and whether the resource conforms to the specified profile. As mentioned in the [HL7 FHIR specifications](https://www.hl7.org/fhir/resource-operation-validate.html), you can also specify the mode for `$validate`, such as create and update:
+Azure API for FHIR will always return an `OperationOutcome` as the validation results for $validate operation. Azure API for FHIR service does two step validation, once a resource is passed into $validate endpoint - the first step is a basic validation to ensure resource can be parsed. During resource parsing, individual errors need to be fixed before proceeding further to next step. Once resource is successfully parsed, full validation is conducted as second step.
 
-- `create`: Azure API for FHIR checks that the profile content is unique from the existing resources and that it's acceptable to be created as a new resource.
-- `update`: Checks that the profile is an update against the nominated existing resource (that is no changes are made to the immutable fields).
-
-Azure API for FHIR will always return an `OperationOutcome` as the validation results.
+> [!NOTE]
+> Any valuesets that are to be used for validation must be uploaded to the FHIR server.  This includes any Valuesets which are part of the FHIR specification, > as well as any ValueSets defined in Implementation Guides.  Only fully expanded Valuesets which contain a full list of all codes are supported.  Any          > ValueSet definitions which reference external sources are not supported
 
 ## Validating an existing resource
 
@@ -107,6 +102,7 @@ If you'd like to specify a profile as a parameter, you can specify the canonical
 
 `GET https://myAzureAPIforFHIR.azurehealthcareapis.com/Observation/12345678/$validate?profile=http://hl7.org/fhir/StructureDefinition/heartrate`
 
+
 ## Validating a new resource
 
 If you'd like to validate a new resource that you're uploading to Azure API for FHIR, you can do a `POST` request:
@@ -119,9 +115,9 @@ For example:
 
 This request will first validate the resource. New resource you're specifying in the request will be created after validation. The server will always return an OperationOutcome as the result.
 
-## Validate on resource CREATE or resource UPDATE
+## Validate on resource CREATE/ UPDATE using header.
 
-You can choose when you'd like to validate your resource, such as on resource `CREATE` or `UPDATE`. By default, Azure API for FHIR is configured to opt out of validation on resource `Create/Update`. To validate on `Create/Update`, you can use the `x-ms-profile-validation` header set to true: `x-ms-profile-validation: true`.
+By default, Azure API for FHIR is configured to opt out of validation on resource `Create/Update`. This capability allows to validate on `Create/Update`, using the `x-ms-profile-validation` header. Set `x-ms-profile-validation' to true for validation.
 
 > [!NOTE]
 > In the open-source FHIR service, you can change the server configuration setting, under the CoreFeatures.

articles/healthcare-apis/azure-api-for-fhir/validation-against-profiles.md

@@ -8,7 +8,7 @@ ms.reviewer: franksolomon
 ms.service: machine-learning
 ms.subservice: mldata
 ms.topic: how-to
-ms.date: 02/07/2022
+ms.date: 02/07/2023
 ---
 
 # Add users to your data labeling project
@@ -39,7 +39,7 @@ To add a custom role, you must have `Microsoft.Authorization/roleAssignments/wri
 1. In the **Description** box, add a description. For example, **Labeler access for data labeling projects**.
 1. Select **Start from JSON**.
 1. At the bottom of the page, select **Next**.
-1. Don't do anything for the **Permissions** tab. You'll add permissions in a later step. Select **Next**.
+1. Don't do anything for the **Permissions** tab. You add permissions in a later step. Select **Next**.
 1. The **Assignable scopes** tab shows your subscription information. Select **Next**.
 1. In the **JSON** tab, above the edit box, select **Edit**.
 1. Select lines starting with **"actions:"** and **"notActions:"**.
@@ -59,7 +59,7 @@ To add a custom role, you must have `Microsoft.Authorization/roleAssignments/wri
 
 ## Add guest user
 
-If your labelers are outside of your organization, you'll now add them so that they can access your workspace. If labelers are already inside your organization, skip this step. 
+If your labelers are outside of your organization, add them, so they can access your workspace. If labelers are already inside your organization, skip this step.
 
 To add a guest user, your organization's external collaboration settings need the correct configuration to allow you to invite guests.
 

articles/machine-learning/how-to-add-users.md

@@ -48,10 +48,10 @@ Image data can be files with any of these types: ".jpg", ".jpeg", ".png", ".jpe"
 
     :::image type="content" source="media/how-to-create-labeling-projects/labeling-creation-wizard.png" alt-text="Labeling project creation for mage labeling":::
 
-    * Choose **Image Classification Multi-class** for projects, when you want to apply only a *single label*, from a set of labels, to an image.
-    * Choose **Image Classification Multi-label** for projects when you want to apply *one or more* labels from a set of labels to an image. For example, a photo of a dog might be labeled with both *dog* and *daytime*.
-    * Choose **Object Identification (Bounding Box)** for those projects where you want to assign a label, and a bounding box, to each object within an image.
-    * Choose **Instance Segmentation (Polygon)** for projects where you want to both assign a label, and draw a polygon, around each object within an image.
+    * Choose **Image Classification Multi-class** for those projects that involve the application of only a *single label*, from a set of labels, to an image.
+    * Choose **Image Classification Multi-label** for projects that involve the application of *one or more* labels, from a set of labels, to an image. For example, a photo of a dog might be labeled with both *dog* and *daytime*.
+    * Choose **Object Identification (Bounding Box)** for projects that involve the assignment of a label, and a bounding box, to each object within an image.
+    * Choose **Instance Segmentation (Polygon)** for projects that involve both the assignment of a label to, and a drawn polygon around, each object within an image.
 
 1. Select **Next** when you want to continue.
 
@@ -64,7 +64,7 @@ Image data can be files with any of these types: ".jpg", ".jpeg", ".png", ".jpe"
 If you already created a dataset that contains your data, select it from the **Select an existing dataset** drop-down list. You can also select **Create a dataset** to use an existing Azure datastore, or to upload local files.
 
 > [!NOTE]
-> A project cannot contain more than 500,000 files. If your dataset has more than this, only the first 500,000 files will be loaded.
+> A project cannot contain more than 500,000 files. If your dataset exceeds this file count, only the first 500,000 files will be loaded.
 
 ### Create a dataset from an Azure datastore
 
@@ -79,7 +79,7 @@ To create a dataset from data that you've already stored in Azure Blob storage:
 1. Select **From Azure storage**, then select **Next**.
 1. Select the datastore, then select **Next**.
 1. If your data is in a subfolder within your blob storage, choose **Browse** to select the path.
-    * Append "/**" to the path to include all the files in the subfolders of the selected path.
+    * Append "/**" to the path, to include all the files in the subfolders of the selected path.
     * Append "**/*.*" to include all the data in the current container and its subfolders.
 1. Select **Create**.
 1. Select the data asset you created.
@@ -127,7 +127,7 @@ For bounding boxes, important questions include:
 * How should labelers handle an object that isn't the object class of interest, but has visual similarities to a relevant object type?
 
 > [!NOTE]
-> labelers can select the first 9 labels with number keys 1-9.
+> Labelers can select the first 9 labels with number keys 1-9.
 
 ## Quality control (preview)
 
@@ -140,7 +140,7 @@ For bounding boxes, important questions include:
 
 To accelerate labeling tasks, the **ML-assisted labeling** page lets you trigger automatic machine learning models. Medical images (".dcm") aren't included in assisted labeling.
 
-At the start of your labeling project, the items are shuffled into a random order to reduce potential bias. However, the trained model reflects any biases present in the dataset. For example, if 80% of your items are of a single class, then approximately 80% of the data used to train the model land in that class.
+At the start of your labeling project, the items are shuffled into a random order to reduce potential bias. However, the trained model reflects any biases present in the dataset. For example, if 80% of your items are of a single class, then approximately 80% of the data used to train the model lands in that class.
 
 Select *Enable ML assisted labeling*, and specify a GPU to enable assisted labeling. If you don't have a GPU in your workspace, a GPU cluster is created for you and added to your workspace. The cluster is created with a minimum of zero nodes, which means it costs nothing when not in use.
 
@@ -186,28 +186,30 @@ The **Dashboard** tab shows the progress of the labeling task.
 
 :::image type="content" source="./media/how-to-create-labeling-projects/labeling-dashboard.png" alt-text="Data labeling dashboard":::
 
-The progress charts show how many items have been labeled, skipped, in need of review, or not yet complete. Hover over the chart to see the number of items in each section.
+The progress charts show how many items have been labeled, skipped, need review, or not yet complete. Hover over the chart to see the number of items in each section.
 
-Below the charts are a distribution of the labels for completed tasks. Remember that in some project types, an item can have multiple labels. Therefore, the total number of labels can exceed the total number items.
+Below the chart 's a distribution of the labels for completed tasks. In some project types, an item can have multiple labels. Therefore, the total number of labels can exceed the total number items.
 
 You also see a distribution of labelers, and how many items they've labeled.
 
-Finally, the middle section shows a table with a queue of tasks not yet assigned. When ML assisted labeling is off, this section shows the number of manual tasks not yet assigned. When ML assisted labeling is on, this section also shows:
+Finally, the middle section shows a table with a queue of unassigned tasks. When ML assisted labeling is off, this section shows the number of manual tasks awaiting assignment.
+
+When ML assisted labeling is on, this section also shows:
 
 * Tasks containing clustered items in the queue
 * Tasks containing prelabeled items in the queue
 
 Additionally, when ML assisted labeling is enabled, you can scroll down to see the ML assisted labeling status. The Jobs sections give links for each of the machine learning runs.
 
 * Training - trains a model to predict the labels
-* Validation - determines whether the prediction of this model will be used to pre-label the items
+* Validation - determines whether item pre-labeling uses the prediction of this model
 * Inference - prediction run for new items
 * Featurization - clusters items (only for image classification projects)
 
 
 ### Data tab
 
-On the **Data** tab, you can see your dataset and review labeled data. Scroll through the labeled data to see the labels. If you see incorrectly labeled data, select it and choose **Reject**, to remove the labels and put the data back into the unlabeled queue.
+On the **Data** tab, you can see your dataset, and review labeled data. Scroll through the labeled data to see the labels. If you see incorrectly labeled data, select it and choose **Reject**, to remove the labels and return the data to the unlabeled queue.
 
 If your project uses consensus labeling, you should review those images that have no consensus:
 
@@ -221,24 +223,23 @@ If your project uses consensus labeling, you should review those images that hav
 
     :::image type="content" source="media/how-to-create-labeling-projects/select-need-review.png" alt-text="Screenshot: Select labels in need of review.":::
 
-1. For each image in need of review, select the **Consensus label** dropdown, to view the conflicting labels.
+1. For each image to review, select the **Consensus label** dropdown, to view the conflicting labels.
 
     :::image type="content" source="media/how-to-create-labeling-projects/consensus-dropdown.png" alt-text="Screenshot: Select Consensus label dropdown to review conflicting labels." lightbox="media/how-to-create-labeling-projects/consensus-dropdown.png":::
 
-1. While you can select an individual to see just their label(s), you can only update or reject the labels from the top choice, **Consensus label (preview)**.
+1. Although you can select an individual to see just their label(s), you can only update or reject the labels from the top choice, **Consensus label (preview)**.
 
 ### Details tab
 
 View and change details of your project. In this tab, you can:
 
 * View project details and input datasets
-* Enable or disable incremental refresh at regular intervals, or request an immediate refresh
+* Enable or disable **incremental refresh at regular intervals**, or request an immediate refresh
 * View details of the storage container used to store labeled outputs in your project
 * Add labels to your project
 * Edit instructions you give to your labels
 * Change settings for ML assisted labeling, and kick off a labeling task
 
-
 ### Access for labelers
 
 [!INCLUDE [access](../../includes/machine-learning-data-labeling-access.md)]