edits

Author: GitHubber17

articles/machine-learning/how-to-mlflow-batch.md

@@ -111,8 +111,6 @@ To create an endpoint, you need a name and description. The endpoint name appear
 
    [!notebook-python[] (~/azureml-examples-main/sdk/python/endpoints/batch/deploy-models/heart-classifier-mlflow/mlflow-for-batch-tabular.ipynb?name=name_endpoint)]
 
-   ---
-
 1. Create the endpoint:
 
    # [Azure CLI](#tab/cli)
@@ -219,10 +217,11 @@ To test your endpoint, you use a sample of unlabeled data located in this reposi
    1. To see the changes, refresh the object:
 
       [!notebook-python[] (~/azureml-examples-main/sdk/python/endpoints/batch/deploy-models/heart-classifier-mlflow/mlflow-for-batch-tabular.ipynb?name=get_data_asset)]
-
-   ---
 
-1. After you upload the data, invoke the endpoint:
+1. After you upload the data, invoke the endpoint.
+
+   > [!TIP]
+   > In the following commands, notice that the deployment name isn't indicated in the `invoke` operation. The endpoint automatically routes the job to the default deployment because the endpoint has one deployment only. You can target a specific deployment by indicating the argument/parameter `deployment_name`.
 
    # [Azure CLI](#tab/cli)
 
@@ -238,11 +237,6 @@ To test your endpoint, you use a sample of unlabeled data located in this reposi
    [!INCLUDE [batch-endpoint-invoke-inputs-sdk](includes/batch-endpoint-invoke-inputs-sdk.md)]
 
    [!notebook-python[] (~/azureml-examples-main/sdk/python/endpoints/batch/deploy-models/heart-classifier-mlflow/mlflow-for-batch-tabular.ipynb?name=start_batch_scoring_job)]
-   
-   ---
-   
-   > [!TIP]
-   > Notice that the deployment name isn't indicated in the `invoke` operation. The endpoint automatically routes the job to the default deployment because the endpoint has one deployment only. You can target a specific deployment by indicating the argument/parameter `deployment_name`.
 
 1. A batch job is started as soon as the command returns. You can monitor the status of the job until it finishes:
 
@@ -307,7 +301,7 @@ The output displays a table:
 | 307 | 0   | heart-unlabeled-3.csv |
 
 > [!TIP]
-> Notice that in this example the input data was tabular data in `CSV` format and there were 4 different input files (heart-unlabeled-0.csv, heart-unlabeled-1.csv, heart-unlabeled-2.csv and heart-unlabeled-3.csv).
+> Notice that in this example, the input data contains tabular data in CSV format. There are four different input files: _heart-unlabeled-0.csv_, _heart-unlabeled-1.csv_, _heart-unlabeled-2.csv_, and _heart-unlabeled-3.csv_.
 
 ## Review considerations for batch inference
 
@@ -404,8 +398,6 @@ Use the following steps to deploy an MLflow model with a custom scoring script:
 
    [!notebook-python[] (~/azureml-examples-main/sdk/python/endpoints/batch/deploy-models/heart-classifier-mlflow/mlflow-for-batch-tabular.ipynb?name=configure_environment_custom)]
 
-   ---
-
 1. Configure the deployment: 
 
    # [Azure CLI](#tab/cli)
@@ -422,8 +414,6 @@ Use the following steps to deploy an MLflow model with a custom scoring script:
 
    [!notebook-python[] (~/azureml-examples-main/sdk/python/endpoints/batch/deploy-models/heart-classifier-mlflow/mlflow-for-batch-tabular.ipynb?name=configure_deployment_custom)]
 
-   ---
-
 1. Create the deployment:
 
    # [Azure CLI](#tab/cli)

articles/machine-learning/how-to-nlp-processing-batch.md

@@ -350,7 +350,7 @@ As mentioned in some of the notes along this tutorial, processing text may have
 
 The same considerations mentioned above apply to MLflow models. However, since you are not required to provide a scoring script for your MLflow model deployment, some of the recommendations mentioned may require a different approach. 
 
-* MLflow models in Batch Endpoints support reading tabular data as input data, which may contain long sequences of text. See [File's types support](how-to-mlflow-batch.md#files-types-support) for details about which file types are supported.
+* MLflow models in Batch Endpoints support reading tabular data as input data, which may contain long sequences of text. See [File's types support](how-to-mlflow-batch.md#review-support-for-file-types) for details about which file types are supported.
 * Batch deployments calls your MLflow model's predict function with the content of an entire file in as Pandas dataframe. If your input data contains many rows, chances are that running a complex model (like the one presented in this tutorial) results in an out-of-memory exception. If this is your case, you can consider:
    * Customize how your model runs predictions and implement batching. To learn how to customize MLflow model's inference, see [Logging custom models](how-to-log-mlflow-models.md?#logging-custom-models).
    * Author a scoring script and load your model using `mlflow.<flavor>.load_model()`. See [Using MLflow models with a scoring script](how-to-mlflow-batch.md#customize-model-deployment-with-scoring-script) for details.

articles/machine-learning/how-to-troubleshoot-batch-endpoints.md

@@ -208,7 +208,7 @@ During script execution for batch deployment, if the `init()` or `run()` functio
 
 ### ValueError: No objects to concatenate
 
-For batch deployment to succeed, each file in a mini-batch must be valid and implement a supported file type. Keep in mind that MLflow models support only a subset of file types. For more information, see [Considerations when deploying to batch inference](how-to-mlflow-batch.md?#considerations-when-deploying-to-batch-inference).
+For batch deployment to succeed, each file in a mini-batch must be valid and implement a supported file type. Keep in mind that MLflow models support only a subset of file types. For more information, see [Considerations when deploying to batch inference](how-to-mlflow-batch.md#review-considerations-for-batch-inference).
 
 **Message logged**: "ValueError: No objects to concatenate."
 
@@ -222,7 +222,7 @@ For batch deployment to succeed, each file in a mini-batch must be valid and imp
 
    1. Look for entries that describe the file input failure, such as "ERROR:azureml:Error processing input file."
 
-   If the file type isn't supported, review the list of supported files. You might need to change the file type of the input data, or customize the deployment by providing a scoring script. For more information, see [Using MLflow models with a scoring script](how-to-mlflow-batch.md?#customizing-mlflow-models-deployments-with-a-scoring-script).
+   If the file type isn't supported, review the list of supported files. You might need to change the file type of the input data, or customize the deployment by providing a scoring script. For more information, see [Using MLflow models with a scoring script](how-to-mlflow-batch.md#customize-model-deployment-with-scoring-script).
 
 ### No succeeded mini-batch
 

articles/machine-learning/includes/batch-endpoint-invoke-inputs-sdk.md

@@ -10,4 +10,4 @@ __What's the difference between the `inputs` and `input` parameter when you invo
 
 In general, you can use a dictionary `inputs = {}` parameter with the `invoke` method to provide an arbitrary number of required inputs to a batch endpoint that contains a _model deployment_ or a _pipeline deployment_.
 
-For a _model deployment_, you can use the `input` parameter as a shorter way to specify the input data location for the deployment. This approach works because a model deployment always takes only one [data input](../how-to-access-data-batch-endpoints-jobs.md#explore-data-inputs).
+For a _model deployment_, you can use the `input` parameter as a shorter way to specify the input data location for the deployment. This approach works because a model deployment always takes only one [data input](../how-to-access-data-batch-endpoints-jobs.md#data-inputs).