fixes

Author: santiagxf

articles/machine-learning/how-to-batch-scoring-script.md

@@ -20,7 +20,7 @@ ms.custom: how-to
 Batch endpoints allow you to deploy models to perform inference at scale. Because how inference should be executed varies from model's format, model's type and use case, batch endpoints require a scoring script (also known as batch driver script) to indicate the deployment how to use the model over the provided data. In this article you will learn how to use scoring scripts in different scenarios and their best practices.
 
 > [!TIP]
-> MLflow models don't require a scoring script as it is autogenerated for you. For more details about how batch endpoints work with MLflow models, see the dedicated tutorial [Using MLflow models in batch deployments](how-to-mlflow-batch.md). Notice that this feature doesn't prevent you from writing an specific scoring script for MLflow models as explained at [Using MLflow models with a scoring script](how-to-mlflow-batch.md#using-mlflow-models-with-a-scoring-script).
+> MLflow models don't require a scoring script as it is autogenerated for you. For more details about how batch endpoints work with MLflow models, see the dedicated tutorial [Using MLflow models in batch deployments](how-to-mlflow-batch.md). Notice that this feature doesn't prevent you from writing an specific scoring script for MLflow models as explained at [Using MLflow models with a scoring script](how-to-mlflow-batch.md#customizing-mlflow-models-deployments-with-a-scoring-script).
 
 > [!WARNING]
 > If you are deploying an Automated ML model under a batch endpoint, notice that the scoring script that Automated ML provides only works for Online Endpoints and it is not designed for batch execution. Please follow this guideline to learn how to create one depending on what your model does.

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

@@ -280,7 +280,7 @@ Once your deployment completes, your deployment is ready to serve request. One o
 :::code language="json" source="~/azureml-examples-main/cli/endpoints/online/mlflow/sample-request-sklearn.json":::
 
 > [!NOTE]
-> Notice how the key `input_data` has been used in this example instead of `inputs` as used in MLflow serving. This is because Azure Machine Learning requires a different input format to be able to automatically generate the swagger contracts for the endpoints. See [Considerations when deploying to real time inference](how-to-deploy-mlflow-models.md#considerations-when-deploying-to-real-time-inference) for details about expected input format.
+> Notice how the key `input_data` has been used in this example instead of `inputs` as used in MLflow serving. This is because Azure Machine Learning requires a different input format to be able to automatically generate the swagger contracts for the endpoints. See [Differences between models deployed in Azure Machine Learning and MLflow built-in server](how-to-deploy-mlflow-models.md#differences-between-models-deployed-in-azure-machine-learning-and-mlflow-built-in-server) for details about expected input format.
 
 To submit a request to the endpoint, you can do as follows:
 

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

@@ -80,7 +80,7 @@ Online endpoints are endpoints that are used for online (real-time) inferencing.
 
 We are going to exploit this functionality by deploying multiple versions of the same model under the same endpoint. However, the new deployment will receive 0% of the traffic at the begging. Once we are sure about the new model to work correctly, we are going to progressively move traffic from one deployment to the other.
 
-#. Endpoints require a name, which needs to be unique in the same region. Let's ensure to create one that doesn't exist:
+1. Endpoints require a name, which needs to be unique in the same region. Let's ensure to create one that doesn't exist:
 
     # [Azure CLI](#tab/cli)
 
@@ -117,7 +117,7 @@ We are going to exploit this functionality by deploying multiple versions of the
     print(f"Endpoint name: {endpoint_name}")
     ```
 
-#. Configure the endpoint
+1. Configure the endpoint
 
     # [Azure CLI](#tab/cli)
     
@@ -155,7 +155,7 @@ We are going to exploit this functionality by deploying multiple versions of the
         outfile.write(json.dumps(endpoint_config))
     ```
 
-#. Create the endpoint:
+1. Create the endpoint:
 
     # [Azure CLI](#tab/cli)
     
@@ -178,7 +178,7 @@ We are going to exploit this functionality by deploying multiple versions of the
     )
     ```
 
-#. Getting the authentication secret for the endpoint.
+1. Getting the authentication secret for the endpoint.
 
     # [Azure CLI](#tab/cli)
     
@@ -206,7 +206,7 @@ We are going to exploit this functionality by deploying multiple versions of the
 
 So far, the endpoint is empty. There are no deployments on it. Let's create the first one by deploying the same model we were working on before. We will call this deployment "default" and it will represent our "blue deployment".
 
-#. Configure the deployment
+1. Configure the deployment
 
     # [Azure CLI](#tab/cli)
     
@@ -262,7 +262,7 @@ So far, the endpoint is empty. There are no deployments on it. Let's create the
         outfile.write(json.dumps(deploy_config))
     ```
 
-#. Create the deployment
+1. Create the deployment
 
     # [Azure CLI](#tab/cli)
     
@@ -287,7 +287,7 @@ So far, the endpoint is empty. There are no deployments on it. Let's create the
     )    
     ```
 
-#. Test the deployment
+1. Test the deployment
 
     # [Azure CLI](#tab/cli)
     
@@ -322,7 +322,7 @@ So far, the endpoint is empty. There are no deployments on it. Let's create the
 
 Let's imagine that there is a new version of the model created by the development team and it is ready to be in production. We can first try to fly this model and once we are confident, we can update the endpoint to route the traffic to it.
 
-#. Register a new model version
+1. Register a new model version
 
     # [Azure CLI](#tab/cli)
     
@@ -354,7 +354,7 @@ Let's imagine that there is a new version of the model created by the developmen
     version = registered_model.version
     ```
 
-#. Configure a new deployment
+1. Configure a new deployment
 
      # [Azure CLI](#tab/cli)
     
@@ -413,7 +413,7 @@ Let's imagine that there is a new version of the model created by the developmen
         outfile.write(json.dumps(deploy_config))
     ```
 
-#. Create the new deployment
+1. Create the new deployment
 
     # [Azure CLI](#tab/cli)
     
@@ -442,7 +442,7 @@ Let's imagine that there is a new version of the model created by the developmen
 
 One we are confident with the new deployment, we can update the traffic to route some of it to the new deployment. Traffic is configured at the endpoint level:
 
-#. Configure the traffic:
+1. Configure the traffic:
 
     # [Azure CLI](#tab/cli)
     
@@ -470,7 +470,7 @@ One we are confident with the new deployment, we can update the traffic to route
         outfile.write(json.dumps(traffic_config))
     ```
 
-#. Update the endpoint
+1. Update the endpoint
 
     # [Azure CLI](#tab/cli)
     
@@ -493,7 +493,7 @@ One we are confident with the new deployment, we can update the traffic to route
     )
     ```
 
-#. If you decide to switch the entire traffic to the new deployment, update all the traffic:
+1. If you decide to switch the entire traffic to the new deployment, update all the traffic:
 
     # [Azure CLI](#tab/cli)
     
@@ -521,7 +521,7 @@ One we are confident with the new deployment, we can update the traffic to route
         outfile.write(json.dumps(traffic_config))
     ```
 
-#. Update the endpoint
+1. Update the endpoint
 
     # [Azure CLI](#tab/cli)
     
@@ -544,7 +544,7 @@ One we are confident with the new deployment, we can update the traffic to route
     )
     ```
 
-#. Since the old deployment doesn't receive any traffic, you can safely delete it:
+1. Since the old deployment doesn't receive any traffic, you can safely delete it:
 
     # [Azure CLI](#tab/cli)
     

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

@@ -57,7 +57,7 @@ A solution to this scenario is to implement machine learning pipelines that move
 
 ### Customize inference with a scoring script
 
-If you want to customize how inference is executed for MLflow models (or opt-out for no-code deployment) you can refer to [Customizing MLflow model deployments (Online Endpoints)](how-to-deploy-mlflow-models-online-endpoints.md#customizing-mlflow-model-deployments) and [Customizing MLflow model deployments (Batch Endpoints)](how-to-mlflow-batch.md#using-mlflow-models-with-a-scoring-script).
+If you want to customize how inference is executed for MLflow models (or opt-out for no-code deployment) you can refer to [Customizing MLflow model deployments (Online Endpoints)](how-to-deploy-mlflow-models-online-endpoints.md#customizing-mlflow-model-deployments) and [Customizing MLflow model deployments (Batch Endpoints)](how-to-mlflow-batch.md#customizing-mlflow-models-deployments-with-a-scoring-script).
 
 > [!IMPORTANT]
 > When you opt-in to indicate a scoring script, you also need to provide an environment for deployment.
@@ -78,7 +78,7 @@ Each workflow has different capabilities, particularly around which type of comp
 | Deploy MLflow models to managed online endpoints | [See example](how-to-deploy-mlflow-models-online-progressive.md)<sup>1</sup> | [See example](how-to-deploy-mlflow-models-online-endpoints.md)<sup>1</sup> | [See example](how-to-deploy-mlflow-models-online-endpoints.md?tabs=studio)]<sup>1</sup> |
 | Deploy MLflow models to managed online endpoints (with a scoring script) | Not supported | [See example](how-to-deploy-mlflow-models-online-endpoints.md#customizing-mlflow-model-deployments) | Not supported  |
 | Deploy MLflow models to batch endpoints |  | [See example](how-to-mlflow-batch.md) | [See example](how-to-mlflow-batch.md?tab=studio) |
-| Deploy MLflow models to batch endpoints (with a scoring script) |  | [See example](how-to-mlflow-batch.md#using-mlflow-models-with-a-scoring-script) | Not supported  |
+| Deploy MLflow models to batch endpoints (with a scoring script) |  | [See example](how-to-mlflow-batch.md#customizing-mlflow-models-deployments-with-a-scoring-script) | Not supported  |
 | Deploy MLflow models to web services (ACI/AKS) | Supported<sup>2</sup> | <sup>2</sup> | <sup>2</sup> |
 | Deploy MLflow models to web services (ACI/AKS - with a scoring script) | <sup>2</sup> | <sup>2</sup> | Supported<sup>2</sup> |
 
@@ -91,7 +91,7 @@ Each workflow has different capabilities, particularly around which type of comp
 If you are familiar with MLflow or your platform support MLflow natively (like Azure Databricks) and you wish to continue using the same set of methods, use the MLflow SDK. On the other hand, if you are more familiar with the [Azure ML CLI v2](concept-v2.md), you want to automate deployments using automation pipelines, or you want to keep deployments configuration in a git repository; we recommend you to use the [Azure ML CLI v2](concept-v2.md). If you want to quickly deploy and test models trained with MLflow, you can use [Azure Machine Learning studio](https://ml.azure.com) UI deployment.
 
 
-## Differences between MLflow models deployed in Azure Machine Learning and MLflow built-in server
+## Differences between models deployed in Azure Machine Learning and MLflow built-in server
 
 MLflow includes built-in deployment tools that model developers can use to test models locally. For instance, you can run a local instance of a model registered in MLflow server registry with `mlflow models serve -m my_model`. Since Azure Machine Learning online endpoints run our influencing server technology, the behavior of these two services is different.
 

articles/machine-learning/toc.yml

@@ -622,6 +622,7 @@
                 - name: Text processing with batch deployments
                   href: how-to-nlp-processing-batch.md
             - name: Integrations
+              items:
                 - name: Invoke batch endpoints from Azure Data Factory
                   href: how-to-use-batch-azure-data-factory.md
                 - name: Invoke batch endpoints from Event Grid events in storage