Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into fixPV2R

Author: roygara

.openpublishing.redirection.azure-monitor.json

@@ -6878,6 +6878,16 @@
           "source_path_from_root": "/articles/azure-monitor/containers/prometheus-authorization-proxy.md",
           "redirect_url": "/previous-versions/azure/azure-monitor/containers/prometheus-authorization-proxy",
           "redirect_document_id": false
+        },
+        {
+          "source_path_from_root": "/articles/azure-monitor/containers/container-insights-private-link.md",
+          "redirect_url": "/previous-versions/azure/azure-monitor/containers/kubernetes-monitoring-private-link",
+          "redirect_document_id": false
+        },
+        {
+          "source_path_from_root": "/articles/azure-monitor/essentials/private-link-data-ingestion.md",
+          "redirect_url": "/previous-versions/azure/azure-monitor/containers/kubernetes-monitoring-private-link",
+          "redirect_document_id": false
         }
     ]
 }

.openpublishing.redirection.virtual-desktop.json

@@ -439,6 +439,11 @@
             "source_path_from_root": "/articles/virtual-desktop/disaster-recovery.md",
             "redirect_url": "/azure/virtual-desktop/disaster-recovery-concepts",
             "redirect_document_id": true
+        },
+        {
+            "source_path_from_root": "/articles/virtual-desktop/configure-device-redirections.md",
+            "redirect_url": "/azure/virtual-desktop/redirection-remote-desktop-protocol",
+            "redirect_document_id": true
         }
     ]
 }

articles/active-directory-b2c/partner-keyless.md

@@ -1,13 +1,13 @@
 ---
 title: Tutorial to configure Keyless with Azure Active Directory B2C
 titleSuffix: Azure AD B2C
-description: Tutorial to configure Sift Keyless with Azure Active Directory B2C for passwordless authentication 
+description: Tutorial to configure Keyless with Azure Active Directory B2C for passwordless authentication 
 author: gargi-sinha
 manager: martinco
 ms.reviewer: kengaderdus
 ms.service: active-directory
 ms.topic: how-to
-ms.date: 06/21/2024
+ms.date: 08/09/2024
 
 ms.author: gasinh
 ms.subservice: B2C
@@ -18,11 +18,11 @@ ms.subservice: B2C
 
 # Tutorial: Configure Keyless with Azure Active Directory B2C
 
-Learn to configure Azure Active Directory B2C (Azure AD B2C) with the Sift Keyless passwordless solution. With Azure AD B2C as an identity provider (IdP), integrate Keyless with customer applications to provide passwordless authentication. The Keyless Zero-Knowledge Biometric (ZKB) is passwordless multifactor authentication that helps eliminate fraud, phishing, and credential reuse, while enhancing the customer experience and protecting privacy.
+Learn to configure Azure Active Directory B2C (Azure AD B2C) with the Keyless passwordless solution. With Azure AD B2C as an identity provider (IdP), integrate Keyless with customer applications to provide passwordless authentication. The Keyless Zero-Knowledge Biometric (ZKB) is passwordless multifactor authentication that helps eliminate fraud, phishing, and credential reuse, while enhancing the customer experience and protecting privacy.
 
 Go to keyless.io to learn about: 
 
-* [Sift Keyless](https://keyless.io/)
+* [Keyless](https://keyless.io/)
 * [How Keyless uses zero-knowledge proofs to protect your biometric data](https://keyless.io/blog/post/how-keyless-uses-zero-knowledge-proofs-to-protect-your-biometric-data)
 
 ## Prerequisites
@@ -42,7 +42,7 @@ The Keyless integration includes the following components:
 
 * **Azure AD B2C** – authorization server that verifies user credentials. Also known as the IdP.
 * **Web and mobile applications** – mobile or web applications to protect with Keyless and Azure AD B2C
-* **The Keyless Authenticator mobile app** – Sift mobile app for authentication to the Azure AD B2C enabled applications
+* **The Keyless Authenticator mobile app** – mobile app for authentication to the Azure AD B2C enabled applications
 
 The following architecture diagram illustrates an implementation.
 

articles/ai-studio/how-to/deploy-models-cohere-command.md

@@ -135,7 +135,7 @@ The response is as follows:
 ```python
 print("Model name:", model_info.model_name)
 print("Model type:", model_info.model_type)
-print("Model provider name:", model_info.model_provider)
+print("Model provider name:", model_info.model_provider_name)
 ```
 
 ```console
@@ -209,14 +209,12 @@ To visualize the output, define a helper function to print the stream.
 ```python
 def print_stream(result):
     """
-    Prints the chat completion with streaming. Some delay is added to simulate 
-    a real-time conversation.
+    Prints the chat completion with streaming.
     """
     import time
     for update in result:
         if update.choices:
             print(update.choices[0].delta.content, end="")
-            time.sleep(0.05)
 ```
 
 You can visualize how streaming generates content:
@@ -1364,7 +1362,7 @@ catch (RequestFailedException ex)
 {
     if (ex.ErrorCode == "content_filter")
     {
-        Console.WriteLine($"Your query has trigger Azure Content Safeaty: {ex.Message}");
+        Console.WriteLine($"Your query has trigger Azure Content Safety: {ex.Message}");
     }
     else
     {

articles/ai-studio/how-to/deploy-models-jais.md

@@ -27,6 +27,8 @@ JAIS 30b Chat is an autoregressive bi-lingual LLM for **Arabic** & **English**.
 
 ::: zone pivot="programming-language-python"
 
+## Jais chat models
+
 
 
 You can learn more about the models in their respective model card:
@@ -103,7 +105,7 @@ The response is as follows:
 ```python
 print("Model name:", model_info.model_name)
 print("Model type:", model_info.model_type)
-print("Model provider name:", model_info.model_provider)
+print("Model provider name:", model_info.model_provider_name)
 ```
 
 ```console
@@ -177,14 +179,12 @@ To visualize the output, define a helper function to print the stream.
 ```python
 def print_stream(result):
     """
-    Prints the chat completion with streaming. Some delay is added to simulate 
-    a real-time conversation.
+    Prints the chat completion with streaming.
     """
     import time
     for update in result:
         if update.choices:
             print(update.choices[0].delta.content, end="")
-            time.sleep(0.05)
 ```
 
 You can visualize how streaming generates content:
@@ -278,6 +278,8 @@ except HttpResponseError as ex:
 
 ::: zone pivot="programming-language-javascript"
 
+## Jais chat models
+
 
 
 You can learn more about the models in their respective model card:
@@ -550,6 +552,8 @@ catch (error) {
 
 ::: zone pivot="programming-language-csharp"
 
+## Jais chat models
+
 
 
 You can learn more about the models in their respective model card:
@@ -821,7 +825,7 @@ catch (RequestFailedException ex)
 {
     if (ex.ErrorCode == "content_filter")
     {
-        Console.WriteLine($"Your query has trigger Azure Content Safeaty: {ex.Message}");
+        Console.WriteLine($"Your query has trigger Azure Content Safety: {ex.Message}");
     }
     else
     {
@@ -838,6 +842,8 @@ catch (RequestFailedException ex)
 
 ::: zone pivot="programming-language-rest"
 
+## Jais chat models
+
 
 
 You can learn more about the models in their respective model card:

articles/ai-studio/how-to/deploy-models-jamba.md

@@ -26,6 +26,8 @@ The Jamba-Instruct model is AI21's production-grade Mamba-based large language m
 
 ::: zone pivot="programming-language-python"
 
+## Jamba-Instruct chat models
+
 
 
 You can learn more about the models in their respective model card:
@@ -102,7 +104,7 @@ The response is as follows:
 ```python
 print("Model name:", model_info.model_name)
 print("Model type:", model_info.model_type)
-print("Model provider name:", model_info.model_provider)
+print("Model provider name:", model_info.model_provider_name)
 ```
 
 ```console
@@ -176,14 +178,12 @@ To visualize the output, define a helper function to print the stream.
 ```python
 def print_stream(result):
     """
-    Prints the chat completion with streaming. Some delay is added to simulate 
-    a real-time conversation.
+    Prints the chat completion with streaming.
     """
     import time
     for update in result:
         if update.choices:
             print(update.choices[0].delta.content, end="")
-            time.sleep(0.05)
 ```
 
 You can visualize how streaming generates content:
@@ -277,6 +277,8 @@ except HttpResponseError as ex:
 
 ::: zone pivot="programming-language-javascript"
 
+## Jamba-Instruct chat models
+
 
 
 You can learn more about the models in their respective model card:
@@ -549,6 +551,8 @@ catch (error) {
 
 ::: zone pivot="programming-language-csharp"
 
+## Jamba-Instruct chat models
+
 
 
 You can learn more about the models in their respective model card:
@@ -820,7 +824,7 @@ catch (RequestFailedException ex)
 {
     if (ex.ErrorCode == "content_filter")
     {
-        Console.WriteLine($"Your query has trigger Azure Content Safeaty: {ex.Message}");
+        Console.WriteLine($"Your query has trigger Azure Content Safety: {ex.Message}");
     }
     else
     {
@@ -837,6 +841,8 @@ catch (RequestFailedException ex)
 
 ::: zone pivot="programming-language-rest"
 
+## Jamba-Instruct chat models
+
 
 
 You can learn more about the models in their respective model card:

articles/ai-studio/how-to/deploy-models-llama.md

@@ -159,7 +159,7 @@ The response is as follows:
 ```python
 print("Model name:", model_info.model_name)
 print("Model type:", model_info.model_type)
-print("Model provider name:", model_info.model_provider)
+print("Model provider name:", model_info.model_provider_name)
 ```
 
 ```console
@@ -233,14 +233,12 @@ To visualize the output, define a helper function to print the stream.
 ```python
 def print_stream(result):
     """
-    Prints the chat completion with streaming. Some delay is added to simulate 
-    a real-time conversation.
+    Prints the chat completion with streaming.
     """
     import time
     for update in result:
         if update.choices:
             print(update.choices[0].delta.content, end="")
-            time.sleep(0.05)
 ```
 
 You can visualize how streaming generates content:
@@ -1038,7 +1036,7 @@ catch (RequestFailedException ex)
 {
     if (ex.ErrorCode == "content_filter")
     {
-        Console.WriteLine($"Your query has trigger Azure Content Safeaty: {ex.Message}");
+        Console.WriteLine($"Your query has trigger Azure Content Safety: {ex.Message}");
     }
     else
     {

articles/ai-studio/how-to/deploy-models-mistral-nemo.md

@@ -113,7 +113,7 @@ The response is as follows:
 ```python
 print("Model name:", model_info.model_name)
 print("Model type:", model_info.model_type)
-print("Model provider name:", model_info.model_provider)
+print("Model provider name:", model_info.model_provider_name)
 ```
 
 ```console
@@ -187,14 +187,12 @@ To visualize the output, define a helper function to print the stream.
 ```python
 def print_stream(result):
     """
-    Prints the chat completion with streaming. Some delay is added to simulate 
-    a real-time conversation.
+    Prints the chat completion with streaming.
     """
     import time
     for update in result:
         if update.choices:
             print(update.choices[0].delta.content, end="")
-            time.sleep(0.05)
 ```
 
 You can visualize how streaming generates content:
@@ -1385,7 +1383,7 @@ catch (RequestFailedException ex)
 {
     if (ex.ErrorCode == "content_filter")
     {
-        Console.WriteLine($"Your query has trigger Azure Content Safeaty: {ex.Message}");
+        Console.WriteLine($"Your query has trigger Azure Content Safety: {ex.Message}");
     }
     else
     {

articles/ai-studio/how-to/deploy-models-mistral-open.md

@@ -158,7 +158,7 @@ The response is as follows:
 ```python
 print("Model name:", model_info.model_name)
 print("Model type:", model_info.model_type)
-print("Model provider name:", model_info.model_provider)
+print("Model provider name:", model_info.model_provider_name)
 ```
 
 ```console
@@ -235,14 +235,12 @@ To visualize the output, define a helper function to print the stream.
 ```python
 def print_stream(result):
     """
-    Prints the chat completion with streaming. Some delay is added to simulate 
-    a real-time conversation.
+    Prints the chat completion with streaming.
     """
     import time
     for update in result:
         if update.choices:
             print(update.choices[0].delta.content, end="")
-            time.sleep(0.05)
 ```
 
 You can visualize how streaming generates content:

articles/ai-studio/how-to/deploy-models-mistral.md

@@ -143,7 +143,7 @@ The response is as follows:
 ```python
 print("Model name:", model_info.model_name)
 print("Model type:", model_info.model_type)
-print("Model provider name:", model_info.model_provider)
+print("Model provider name:", model_info.model_provider_name)
 ```
 
 ```console
@@ -217,14 +217,12 @@ To visualize the output, define a helper function to print the stream.
 ```python
 def print_stream(result):
     """
-    Prints the chat completion with streaming. Some delay is added to simulate 
-    a real-time conversation.
+    Prints the chat completion with streaming.
     """
     import time
     for update in result:
         if update.choices:
             print(update.choices[0].delta.content, end="")
-            time.sleep(0.05)
 ```
 
 You can visualize how streaming generates content:
@@ -1475,7 +1473,7 @@ catch (RequestFailedException ex)
 {
     if (ex.ErrorCode == "content_filter")
     {
-        Console.WriteLine($"Your query has trigger Azure Content Safeaty: {ex.Message}");
+        Console.WriteLine($"Your query has trigger Azure Content Safety: {ex.Message}");
     }
     else
     {