Merge pull request #1895 from Emma-yxf/2024_12-Monthly-broken-links-fix-lgayhardt

2024_12-Monthly-broken-links-fix-lgayhardt

Author: prmerger-automator[bot]

articles/ai-studio/concepts/evaluation-approach-gen-ai.md

@@ -38,7 +38,7 @@ Key considerations at this stage might include:
 - **Bias and ethical considerations**: Does the model produce any outputs that might perpetuate or promote harmful stereotypes?
 - **Risk and safety**: Are there any risks of the model generating unsafe or malicious content?
 
-You can explore [Azure AI Foundry benchmarks](./model-benchmarks.md)to evaluate and compare models on publicly available datasets, while also regenerating benchmark results on your own data. Alternatively, you can evaluate one of many base generative AI models via Azure AI Evaluation SDK as demonstrated, see [Evaluate model endpoints sample](https://github.com/Azure-Samples/azureai-samples/blob/main/scenarios/evaluate/evaluate_endpoints/evaluate_endpoints.ipynb).
+You can explore [Azure AI Foundry benchmarks](./model-benchmarks.md)to evaluate and compare models on publicly available datasets, while also regenerating benchmark results on your own data. Alternatively, you can evaluate one of many base generative AI models via Azure AI Evaluation SDK as demonstrated, see [Evaluate model endpoints sample](https://github.com/Azure-Samples/azureai-samples/blob/main/scenarios/evaluate/Supported_Evaluation_Targets/Evaluate_Base_Model_Endpoint/Evaluate_Base_Model_Endpoint.ipynb).
 
 ## Pre-production evaluation
 

articles/machine-learning/v1/how-to-use-mlflow.md

@@ -31,7 +31,7 @@ See [MLflow and Azure Machine Learning](../concept-mlflow.md) for all supported
 ## Prerequisites
 
 * Install the `mlflow` package. 
-    * You can use the [MLflow Skinny](https://github.com/mlflow/mlflow/blob/master/README_SKINNY.rst) which is a lightweight MLflow package without SQL storage, server, UI, or data science dependencies. This is recommended for users who primarily need the tracking and logging capabilities without importing the full suite of MLflow features including deployments.
+    * You can use the [MLflow Skinny](https://github.com/mlflow/mlflow/blob/master/README_SKINNY.md) which is a lightweight MLflow package without SQL storage, server, UI, or data science dependencies. This is recommended for users who primarily need the tracking and logging capabilities without importing the full suite of MLflow features including deployments.
 
 * Install the `azureml-mlflow` package. 
 * [Create an Azure Machine Learning Workspace](../quickstart-create-resources.md).