Merge pull request #2166 from Albertyang0/2025_01-Monthly-broken-links-fix-msakande

2025_01 - Fix monthly broken links - msakande

Author: prmerger-automator[bot]

articles/machine-learning/how-to-deploy-mlflow-models-online-endpoints.md

@@ -25,7 +25,7 @@ For no-code-deployment, Azure Machine Learning:
 * Dynamically installs Python packages provided in the `conda.yaml` file. Hence, dependencies get installed during container runtime.
 * Provides an MLflow base image/curated environment that contains the following items:
     * [`azureml-inference-server-http`](how-to-inference-server-http.md) 
-    * [`mlflow-skinny`](https://github.com/mlflow/mlflow/blob/master/README_SKINNY.rst)
+    * [`mlflow-skinny`](https://github.com/mlflow/mlflow/blob/master/README_SKINNY.md)
     * A scoring script for inferencing.
 
 [!INCLUDE [mlflow-model-package-for-workspace-without-egress](includes/mlflow-model-package-for-workspace-without-egress.md)]

articles/machine-learning/how-to-deploy-mlflow-models.md

@@ -29,7 +29,7 @@ For no-code deployment, Azure Machine Learning:
 
 - Ensures that all the package dependencies indicated in the MLflow model are satisfied.
 - Provides an MLflow base image or curated environment that contains the following items:
-  - Packages required for Azure Machine Learning to perform inference, including [`mlflow-skinny`](https://github.com/mlflow/mlflow/blob/master/README_SKINNY.rst).
+  - Packages required for Azure Machine Learning to perform inference, including [`mlflow-skinny`](https://github.com/mlflow/mlflow/blob/master/README_SKINNY.md).
   - A scoring script to perform inference.
 
 [!INCLUDE [mlflow-model-package-for-workspace-without-egress](includes/mlflow-model-package-for-workspace-without-egress.md)]

articles/machine-learning/how-to-use-mlflow-configure-tracking.md

@@ -35,7 +35,7 @@ However, if you work outside Azure Machine Learning, you need to configure MLflo
   ```
 
   > [!TIP]
-  > Instead of `mlflow`, consider using [`mlflow-skinny`](https://github.com/mlflow/mlflow/blob/master/README_SKINNY.rst). This package is a lightweight MLflow package without SQL storage, server, UI, or data science dependencies. It's recommended for users who primarily need MLflow tracking and logging capabilities but don't want to import the full suite of features, including deployments.
+  > Instead of `mlflow`, consider using [`mlflow-skinny`](https://github.com/mlflow/mlflow/blob/master/README_SKINNY.md). This package is a lightweight MLflow package without SQL storage, server, UI, or data science dependencies. It's recommended for users who primarily need MLflow tracking and logging capabilities but don't want to import the full suite of features, including deployments.
 
 - An Azure Machine Learning workspace. To create a workspace, see [Create resources you need to get started](quickstart-create-resources.md).
 

articles/machine-learning/includes/machine-learning-mlflow-prereqs.md

@@ -13,7 +13,7 @@ ms.author: fasantia
   ```
 
   > [!TIP]
-  > You can use the [`mlflow-skinny`](https://github.com/mlflow/mlflow/blob/master/README_SKINNY.rst) package, which is a lightweight MLflow package without SQL storage, server, UI, or data science dependencies. This package is recommended for users who primarily need the MLflow tracking and logging capabilities without importing the full suite of features, including deployments.
+  > You can use the [`mlflow-skinny`](https://github.com/mlflow/mlflow/blob/master/README_SKINNY.md) package, which is a lightweight MLflow package without SQL storage, server, UI, or data science dependencies. This package is recommended for users who primarily need the MLflow tracking and logging capabilities without importing the full suite of features, including deployments.
 
 - Create an Azure Machine Learning workspace. To create a workspace, see [Create resources you need to get started](../quickstart-create-resources.md). Review the [access permissions](../how-to-assign-roles.md#mlflow-operations) you need to perform your MLflow operations in your workspace.