Update how-to-mlflow-batch.md

Author: lgayhardt

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

@@ -61,7 +61,7 @@ Follow these steps to deploy an MLflow model to a batch endpoint for running bat
 
     [!notebook-python[] (~/azureml-examples-main/sdk/python/endpoints/batch/deploy-models/heart-classifier-mlflow/mlflow-for-batch-tabular.ipynb?name=register_model)]
 
-1. Before moving any forward, we need to make sure the batch deployments we are about to create can run on some infrastructure (compute). Batch deployments can run on any Azure Machine Learning compute that already exists in the workspace. That means that multiple batch deployments can share the same compute infrastructure. In this example, we are going to work on an Azure Machine Learning compute cluster called `cpu-cluster`. Let's verify the compute exists on the workspace or create it otherwise.
+1. Before moving any forward, we need to make sure the batch deployments we're about to create can run on some infrastructure (compute). Batch deployments can run on any Azure Machine Learning compute that already exists in the workspace. That means that multiple batch deployments can share the same compute infrastructure. In this example, we're going to work on an Azure Machine Learning compute cluster called `cpu-cluster`. Let's verify the compute exists on the workspace or create it otherwise.
 
     # [Azure CLI](#tab/cli)
 
@@ -156,7 +156,7 @@ Follow these steps to deploy an MLflow model to a batch endpoint for running bat
 
 ## Testing out the deployment
 
-For testing our endpoint, we are going to use a sample of unlabeled data located in this repository and that can be used with the model. Batch endpoints can only process data that is located in the cloud and that is accessible from the Azure Machine Learning workspace. In this example, we are going to upload it to an Azure Machine Learning data store. Particularly, we are going to create a data asset that can be used to invoke the endpoint for scoring. However, notice that batch endpoints accept data that can be placed in various locations.
+For testing our endpoint, we're going to use a sample of unlabeled data located in this repository and that can be used with the model. Batch endpoints can only process data that is located in the cloud and that is accessible from the Azure Machine Learning workspace. In this example, we're going to upload it to an Azure Machine Learning data store. Particularly, we're going to create a data asset that can be used to invoke the endpoint for scoring. However, notice that batch endpoints accept data that can be placed in various locations.
 
 1. Let's create the data asset first. This data asset consists of a folder with multiple CSV files that we want to process in parallel using batch endpoints. You can skip this step is your data is already registered as a data asset or you want to use a different input type.
 
@@ -205,7 +205,7 @@ For testing our endpoint, we are going to use a sample of unlabeled data located
     ---
 
     > [!TIP]
-    > Notice how we are not indicating the deployment name in the invoke operation. That's because the endpoint automatically routes the job to the default deployment. Since our endpoint only has one deployment, then that one is the default one. You can target an specific deployment by indicating the argument/parameter `deployment_name`.
+    > Notice how we're not indicating the deployment name in the invoke operation. That's because the endpoint automatically routes the job to the default deployment. Since our endpoint only has one deployment, then that one is the default one. You can target an specific deployment by indicating the argument/parameter `deployment_name`.
 
 3. A batch job is started as soon as the command returns. You can monitor the status of the job until it finishes:
 
@@ -337,7 +337,7 @@ Use the following steps to deploy an MLflow model with a custom scoring script.
 
    :::code language="python" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/heart-classifier-mlflow/deployment-custom/code/batch_driver.py" :::
 
-1. Let's create an environment where the scoring script can be executed. Since our model is MLflow, the conda requirements are also specified in the model package (for more details about MLflow models and the files included on it see [The MLmodel format](concept-mlflow-models.md#the-mlmodel-format)). We are going then to build the environment using the conda dependencies from the file. However, __we need also to include__ the package `azureml-core` which is required for Batch Deployments.
+1. Let's create an environment where the scoring script can be executed. Since our model is MLflow, the conda requirements are also specified in the model package (for more details about MLflow models and the files included on it see [The MLmodel format](concept-mlflow-models.md#the-mlmodel-format)). We're going then to build the environment using the conda dependencies from the file. However, __we need also to include__ the package `azureml-core` which is required for Batch Deployments.
 
     > [!TIP]
     > If your model is already registered in the model registry, you can download/copy the `conda.yml` file associated with your model by going to [Azure Machine Learning studio](https://ml.azure.com) > Models > Select your model from the list > Artifacts. Open the root folder in the navigation and select the `conda.yml` file listed. Click on Download or copy its content.