Merge pull request #1828 from PhilKang0704/broken-link-fix-sdgilley

prmerger-automator[bot] · web-flow · commit d0d0fd1ae12e · 2024-12-11T18:34:35.000Z
Broken links fix - sdgilley
diff --git a/articles/ai-studio/how-to/develop/ai-template-get-started.md b/articles/ai-studio/how-to/develop/ai-template-get-started.md
@@ -35,8 +35,8 @@ Start with our sample applications! Choose the right template for your needs, th
 | [Process Automation: speech to text and summarization with Azure AI Foundry](https://github.com/Azure-Samples/summarization-openai-python-prompflow) | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure OpenAI Service](../../../ai-services/openai/overview.md), [Azure AI speech to text service](../../../ai-services/speech-service/index-speech-to-text.yml), Bicep  | An app for workers to report issues via text or speech, translating audio to text, summarizing it, and specify the relevant department. | 
 | [Multi-Modal Creative Writing copilot with Dalle](https://github.com/Azure-Samples/agent-openai-python-prompty) | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure AI Search](/azure/search/search-what-is-azure-search), [Azure OpenAI Service](../../../ai-services/openai/overview.md), Bicep | demonstrates how to create and work with AI agents. The app takes a topic and instruction input and then calls a research agent, writer agent, and editor agent. |  
 | [Assistant API Analytics Copilot with Python and Azure AI Foundry](https://github.com/Azure-Samples/assistant-data-openai-python-promptflow) | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) |  [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure AI Search](/azure/search/search-what-is-azure-search), [Azure OpenAI Service](../../../ai-services/openai/overview.md), Bicep| A data analytics chatbot based on the Assistants API. The chatbot can answer questions in natural language, and interpret them as queries on an example sales dataset. |
-| [Function Calling with Prompty, LangChain, and Pinecone](https://github.com/Azure-Samples/agent-openai-python-prompty-langchain-pinecone) | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure OpenAI Service](../../../ai-services/openai/overview.md), [LangChain](https://python.langchain.com/v0.1/docs/get_started/introduction), [Pinecone](https://www.pinecone.io/), Bicep  | Utilize the new Prompty tool, LangChain, and Pinecone to build a large language model (LLM) search agent. This agent with Retrieval-Augmented Generation (RAG) technology is capable of answering user questions based on the provided data by integrating real-time information retrieval with generative responses. | 
-| [Function Calling with Prompty, LangChain, and Elastic Search](https://github.com/Azure-Samples/agent-python-openai-prompty-langchain) | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure OpenAI Service](../../../ai-services/openai/overview.md), [Elastic Search](https://www.elastic.co/elasticsearch), [LangChain](https://python.langchain.com/v0.1/docs/get_started/introduction) , Bicep  | Utilize the new Prompty tool, LangChain, and Elasticsearch to build a large language model (LLM) search agent. This agent with Retrieval-Augmented Generation (RAG) technology is capable of answering user questions based on the provided data by integrating real-time information retrieval with generative responses |
+| Function Calling with Prompty, LangChain, and Pinecone | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure OpenAI Service](../../../ai-services/openai/overview.md), [LangChain](https://python.langchain.com/v0.1/docs/get_started/introduction), [Pinecone](https://www.pinecone.io/), Bicep  | Utilize the new Prompty tool, LangChain, and Pinecone to build a large language model (LLM) search agent. This agent with Retrieval-Augmented Generation (RAG) technology is capable of answering user questions based on the provided data by integrating real-time information retrieval with generative responses. | 
+| Function Calling with Prompty, LangChain, and Elastic Search | [Azure AI Foundry online endpoints](/azure/machine-learning/concept-endpoints-online) | [Azure Managed Identity](/entra/identity/managed-identities-azure-resources/overview), [Azure OpenAI Service](../../../ai-services/openai/overview.md), [Elastic Search](https://www.elastic.co/elasticsearch), [LangChain](https://python.langchain.com/v0.1/docs/get_started/introduction) , Bicep  | Utilize the new Prompty tool, LangChain, and Elasticsearch to build a large language model (LLM) search agent. This agent with Retrieval-Augmented Generation (RAG) technology is capable of answering user questions based on the provided data by integrating real-time information retrieval with generative responses |
 
 ### [C#](#tab/csharp)
 
diff --git a/articles/ai-studio/how-to/develop/sdk-overview.md b/articles/ai-studio/how-to/develop/sdk-overview.md
@@ -428,7 +428,7 @@ if application_insights_connection_string:
 
 ::: zone pivot="programming-language-csharp"
 
-Tracing is not yet integrated into the projects package. For instructions on how to instrument and log traces from the Azure AI Inferencing package, see [azure-sdk-for-dotnet](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/ai/Azure.AI.Inference/samples/Sample8_ChatCompletionsWithOpenTelemetry.md.).
+Tracing is not yet integrated into the projects package. For instructions on how to instrument and log traces from the Azure AI Inferencing package, see [azure-sdk-for-dotnet](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/ai/Azure.AI.Inference/samples/Sample8_ChatCompletionsWithOpenTelemetry.md).
 
 ::: zone-end
 
diff --git a/articles/ai-studio/includes/ai-services/code-create-chat-client.md b/articles/ai-studio/includes/ai-services/code-create-chat-client.md
@@ -55,7 +55,7 @@ const client = new ModelClient(
 );
 ```
 
-Explore our [samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/ai/ai-inference-rest/samples) and read the [API reference documentation](https://aka.ms/AAp1kxa) to get yourself started.
+Explore our [samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/ai/ai-inference-rest/samples) and read the [API reference documentation](/azure/ai-services/openai/gpt-v-quickstart) to get yourself started.
 
 # [C#](#tab/csharp)
 
diff --git a/articles/machine-learning/how-to-train-distributed-gpu.md b/articles/machine-learning/how-to-train-distributed-gpu.md
@@ -115,7 +115,7 @@ TF_CONFIG='{
 
 ### TensorFlow example
 
-* For the full notebook to run the TensorFlow example, see [azureml-examples: Train a basic neural network with distributed MPI on the MNIST dataset using Tensorflow with Horovod](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/single-step/tensorflow/mnist-distributed-horovod/tensorflow-mnist-distributed-horovod.ipynb).
+* For the full notebook to run the TensorFlow example, see [azureml-examples: Train a basic neural network with distributed MPI on the MNIST dataset using Tensorflow with Horovod](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/single-step/tensorflow/mnist-distributed/tensorflow-mnist-distributed.ipynb).
 
 ## Accelerating distributed GPU training with InfiniBand
 
diff --git a/articles/machine-learning/v1/how-to-train-tensorflow.md b/articles/machine-learning/v1/how-to-train-tensorflow.md
@@ -35,9 +35,9 @@ Run this code on either of these environments:
 - Your own Jupyter Notebook server
     - [Install the Azure Machine Learning SDK](/python/api/overview/azure/ml/install) (>= 1.15.0).
     - [Create a workspace configuration file](how-to-configure-environment.md).
-    - [Download the sample script files](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow) `tf_mnist.py` and `utils.py`
+    - [Download the sample script files](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow) `tf_mnist.py` and `utils.py`
 
-    You can also find a completed [Jupyter Notebook version](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) of this guide on the GitHub samples page. The notebook includes expanded sections covering intelligent hyperparameter tuning, model deployment, and notebook widgets.
+    You can also find a completed [Jupyter Notebook version](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow/hyperparameter-tune-and-warm-start-with-tensorflow.ipynb) of this guide on the GitHub samples page. The notebook includes expanded sections covering intelligent hyperparameter tuning, model deployment, and notebook widgets.
 
 [!INCLUDE [gpu quota](../includes/machine-learning-gpu-quota-prereq.md)]
 
