fix broken links

Author: msakande

articles/machine-learning/concept-endpoints-batch.md

@@ -38,14 +38,14 @@ A deployment is a set of resources and computes required to implement the functi
 
 Two types of deployments are possible in Azure Machine Learning batch endpoints:
 
-* [Model deployment](#model-deployments)
+* [Model deployment](#model-deployment)
 * [Pipeline component deployment](#pipeline-component-deployment)
 
 ### Model deployment
 
 Model deployment enables the operationalization of model inferencing at scale, allowing you to process large amounts of data in a low latency and asynchronous way. Azure Machine Learning automatically instruments scalability by providing parallelization of the inferencing processes across multiple nodes in a compute cluster.
 
-Use __Model deployments__ when:
+Use __Model deployment__ when:
 
 > [!div class="checklist"]
 > * You have expensive models that require a longer time to run inference.
@@ -69,7 +69,7 @@ To create a model deployment in a batch endpoint, you need to specify the follow
 
 Pipeline component deployment enables the operationalization of entire processing graphs (or pipelines) to perform batch inference in a low latency and asynchronous way.
 
-Use __Pipeline component deployments__ when:
+Use __Pipeline component deployment__ when:
 
 > [!div class="checklist"]
 > * You need to operationalize complete compute graphs that can be decomposed into multiple steps.

articles/machine-learning/how-to-access-data-batch-endpoints-jobs.md

@@ -276,7 +276,7 @@ The following table summarizes the inputs and outputs for batch deployments:
 
 | Deployment type | Input's number | Supported input's types | Output's number | Supported output's types |
 |--|--|--|--|--|
-| [Model deployment](concept-endpoints-batch.md#model-deployments) | 1 | [Data inputs](#data-inputs) | 1 | [Data outputs](#data-outputs) |
+| [Model deployment](concept-endpoints-batch.md#model-deployment) | 1 | [Data inputs](#data-inputs) | 1 | [Data outputs](#data-outputs) |
 | [Pipeline component deployment](concept-endpoints-batch.md#pipeline-component-deployment) | [0..N] | [Data inputs](#data-inputs) and [literal inputs](#literal-inputs) | [0..N] | [Data outputs](#data-outputs) |
 
 > [!TIP]

articles/machine-learning/reference-yaml-deployment-batch.md

@@ -33,7 +33,7 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 | `description` | string | Description of the deployment. | | |
 | `tags` | object | Dictionary of tags for the deployment. | | |
 | `endpoint_name` | string | **Required.** Name of the endpoint to create the deployment under. | | |
-| `type` | string | **Required.** Type of the bath deployment. Use `model` for [model deployments](concept-endpoints-batch.md#model-deployments) and `pipeline` for [pipeline component deployments](concept-endpoints-batch.md#pipeline-component-deployment). <br><br>**New in version 1.7**. | `model`, `pipeline` | `model` |
+| `type` | string | **Required.** Type of the bath deployment. Use `model` for [model deployments](concept-endpoints-batch.md#model-deployment) and `pipeline` for [pipeline component deployments](concept-endpoints-batch.md#pipeline-component-deployment). <br><br>**New in version 1.7**. | `model`, `pipeline` | `model` |
 | `settings` | object | Configuration of the deployment. See specific YAML reference for model and pipeline component for allowed values. <br><br>**New in version 1.7**. | | |
 
 > [!TIP]