Fix suggestion.

Author: TimShererWithAquent

articles/machine-learning/v1/concept-model-management-and-deployment.md

@@ -104,7 +104,7 @@ When you use a model as a web service, provide the following items:
 
 - The models that are used to score data submitted to the service or device.
 - An entry script. This script accepts requests, uses the models to score the data, and return a response.
-- An Azure Machine Learning environment that describes the Pip and Conda dependencies that the models requires an entry script.
+- An Azure Machine Learning environment that describes the Pip and Conda dependencies that the models require an entry script.
 - Any other assets such as text or data that the models require and entry script.
 
 You also provide the configuration of the target deployment platform. For example, the virtual machine family type, available memory, and number of cores when deploying to Azure Kubernetes Service.
@@ -113,7 +113,7 @@ When the image is created, components required by Azure Machine Learning are als
 
 #### Batch scoring
 
-Batch scoring is supported through machine learning pipelines. For more information, see [Tutorial: Build an Azure Machine Learning pipeline for image classification](/v1/tutorial-pipeline-python-sdk.md).
+Batch scoring is supported through machine learning pipelines. For more information, see [Tutorial: Build an Azure Machine Learning pipeline for image classification](tutorial-pipeline-python-sdk.md).
 
 #### Real-time web services