2025_04 - Fix monthly broken links - ms-msakande

Author: Albertyang0

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 that you list in a conda.yaml file. As a result, dependencies are installed during container runtime.
 * Provides an MLflow base image, or curated environment, that contains the following items:
     * The [`azureml-inference-server-http`](how-to-inference-server-http.md) package
-    * The [`mlflow-skinny`](https://github.com/mlflow/mlflow/blob/master/README_SKINNY.md) package
+    * The [`mlflow-skinny`](https://github.com/mlflow/mlflow/blob/master/skinny/README_SKINNY.md) package
     * 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.md).
+  - Packages required for Azure Machine Learning to perform inference, including [`mlflow-skinny`](https://github.com/mlflow/mlflow/blob/master/skinny/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/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.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.
+  > You can use the [`mlflow-skinny`](https://github.com/mlflow/mlflow/blob/master/skinny/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.
 

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

@@ -33,7 +33,7 @@ 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.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.
+    * You can use the [MLflow Skinny](https://github.com/mlflow/mlflow/blob/master/skinny/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).