fixes

Author: santiagxf

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

@@ -433,7 +433,7 @@ The following data types are supported for batch inference when deploying MLflow
 > Be advised that any unsupported file that may be present in the input data will make the job to fail. You will see an error entry as follows: *"ERROR:azureml:Error processing input file: '/mnt/batch/tasks/.../a-given-file.parquet'. File type 'parquet' is not supported."*.
 
 > [!TIP]
-> If you like to process a different file type, or execute inference in a different way that batch endpoints do by default you can always create the deploymnet with a scoring script as explained in [Using MLflow models with a scoring script](#using-mlflow-models-with-a-scoring-script).
+> If you like to process a different file type, or execute inference in a different way that batch endpoints do by default you can always create the deploymnet with a scoring script as explained in [Using MLflow models with a scoring script](#customizing-mlflow-models-deployments-with-a-scoring-script).
 
 ### Signature enforcement for MLflow models
 
@@ -446,7 +446,7 @@ You can inspect the model signature of your model by opening the `MLmodel` file
 
 ### Flavor support
 
-Batch deployments only support deploying MLflow models with a `pyfunc` flavor. If you need to deploy a different flavor, see [Using MLflow models with a scoring script](#using-mlflow-models-with-a-scoring-script).
+Batch deployments only support deploying MLflow models with a `pyfunc` flavor. If you need to deploy a different flavor, see [Using MLflow models with a scoring script](#customizing-mlflow-models-deployments-with-a-scoring-script).
 
 ## Customizing MLflow models deployments with a scoring script
 

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

@@ -260,9 +260,9 @@ As mentioned in some of the notes along this tutorial, processing text may have
 
 MLflow models in Batch Endpoints support reading CSVs as input data, which may contain long sequences of text. 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 recommendation there may be harder to achieve. 
 
-* Only `CSV` files are supported for MLflow deployments processing text. You will need to author a scoring script if you need to process other file types like `TXT`, `PARQUET`, etc. See [Using MLflow models with a scoring script](how-to-mlflow-batch.md#using-mlflow-models-with-a-scoring-script) for details.
+* Only `CSV` files are supported for MLflow deployments processing text. You will need to author a scoring script if you need to process other file types like `TXT`, `PARQUET`, etc. See [Using MLflow models with a scoring script](how-to-mlflow-batch.md#customizing-mlflow-models-deployments-with-a-scoring-script) for details.
 * Batch deployments will call 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) will result 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#using-mlflow-models-with-a-scoring-script) for details.
+   * 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#customizing-mlflow-models-deployments-with-a-scoring-script) for details.
 
 

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

@@ -167,7 +167,7 @@ __Message logged__: ValueError: No objects to concatenate.
 
 __Reason__: All the files in the generated mini-batch are either corrupted or unsupported file types. Remember that MLflow models support a subset of file types as documented at [Considerations when deploying to batch inference](how-to-mlflow-batch.md?#considerations-when-deploying-to-batch-inference).
 
-__Solution__: Go to the file `logs/usr/stdout/<process-number>/process000.stdout.txt` and look for entries like `ERROR:azureml:Error processing input file`. If the file type is not supported, please review the list of supported files. You may need to change the file type of the input data or customize the deployment by providing a scoring script as indicated at [Using MLflow models with a scoring script](how-to-mlflow-batch.md?#using-mlflow-models-with-a-scoring-script).
+__Solution__: Go to the file `logs/usr/stdout/<process-number>/process000.stdout.txt` and look for entries like `ERROR:azureml:Error processing input file`. If the file type is not supported, please review the list of supported files. You may need to change the file type of the input data or customize the deployment by providing a scoring script as indicated at [Using MLflow models with a scoring script](how-to-mlflow-batch.md?#customizing-mlflow-models-deployments-with-a-scoring-script).
 
 ### There is no succeeded mini batch item returned from run()
 

articles/machine-learning/toc.yml

@@ -643,6 +643,7 @@
                 - name: Progressive rollout of MLflow models
                   href: how-to-deploy-mlflow-models-online-progressive.md 
             - name: Batch Inference
+              items:
                 - name: Deploy MLflow models to Batch Endpoints
                   href: how-to-mlflow-batch.md
         - name: Inference HTTP server