Update reference-yaml-deployment-batch.md

Author: santiagxf

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

@@ -30,10 +30,10 @@ 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-preview). Introduced since verison 1.7 and above. | `model`, `pipeline` | `model` |
-| `settings` | object | **Required if type is indicated.** Specific configuration of the deployment. See specific YAML reference for model and pipeline component for allowed values. Introduced since verison 1.7 and above. | | |
+| `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-preview). Introduced since version 1.7. | `model`, `pipeline` | `model` |
+| `settings` | object | **Required if type is indicated.** Specific configuration of the deployment. See specific YAML reference for model and pipeline component for allowed values. Introduced since version 1.7. | | |
 
-> [!NOTE]
+> [!TIP]
 > The key `type` has been introduced in version 1.7 of the CLI extension and above. To fully support backward compatibility, this property defaults to `model`. However, if not explicitly indicated, the key `settings` is not enforced and all the properties for the model deployment settings should be indicated in to root of the YAML specification.
 
 ### YAML syntax for model deployments
@@ -45,12 +45,12 @@ When `type: model`, the following syntax is enforced:
 | `model` | string or object | **Required.** The model to use for the deployment. This value can be either a reference to an existing versioned model in the workspace or an inline model specification. <br><br> To reference an existing model, use the `azureml:<model-name>:<version>` syntax. <br><br> To define a model inline, follow the [Model schema](reference-yaml-model.md#yaml-syntax). <br><br> As a best practice for production scenarios, you should create the model separately and reference it here. | | |
 | `code_configuration` | object | Configuration for the scoring code logic. <br><br> This property is not required if your model is in MLflow format. | | |
 | `code_configuration.code` | string | The local directory that contains all the Python source code to score the model. | | |
-| `code_configuration.scoring_script` | string | The Python file in the above directory. This file must have an `init()` function and a `run()` function. Use the `init()` function for any costly or common preparation (for example, load the model in memory). `init()` will be called only once at beginning of process. Use `run(mini_batch)` to score each entry; the value of `mini_batch` is a list of file paths. The `run()` function should return a pandas DataFrame or an array. Each returned element indicates one successful run of input element in the `mini_batch`. For more information on how to author scoring script, see [Understanding the scoring script](batch-inference/how-to-batch-scoring-script.md#understanding-the-scoring-script).| | |
+| `code_configuration.scoring_script` | string | The Python file in the above directory. This file must have an `init()` function and a `run()` function. Use the `init()` function for any costly or common preparation (for example, load the model in memory). `init()` is called only once at beginning of process. Use `run(mini_batch)` to score each entry; the value of `mini_batch` is a list of file paths. The `run()` function should return a pandas DataFrame or an array. Each returned element indicates one successful run of input element in the `mini_batch`. For more information on how to author scoring script, see [Understanding the scoring script](batch-inference/how-to-batch-scoring-script.md#understanding-the-scoring-script).| | |
 | `environment` | string or object | The environment to use for the deployment. This value can be either a reference to an existing versioned environment in the workspace or an inline environment specification. <br><br> This property is not required if your model is in MLflow format. <br><br> To reference an existing environment, use the `azureml:<environment-name>:<environment-version>` syntax. <br><br> To define an environment inline, follow the [Environment schema](reference-yaml-environment.md#yaml-syntax). <br><br> As a best practice for production scenarios, you should create the environment separately and reference it here. | | |
 | `compute` | string | **Required.** Name of the compute target to execute the batch scoring jobs on. This value should be a reference to an existing compute in the workspace using the `azureml:<compute-name>` syntax. | | |
 | `resources.instance_count` | integer | The number of nodes to use for each batch scoring job. | | `1` |
 | `settings.max_concurrency_per_instance` | integer | The maximum number of parallel `scoring_script` runs per instance. | | `1` |
-| `settings.error_threshold` | integer | The number of file failures that should be ignored. If the error count for the entire input goes above this value, the batch scoring job will be terminated. `error_threshold` is for the entire input and not for individual mini batches. If omitted, any number of file failures will be allowed without terminating the job.  | | `-1` |
+| `settings.error_threshold` | integer | The number of file failures that should be ignored. If the error count for the entire input goes above this value, the batch scoring job is terminated. `error_threshold` is for the entire input and not for individual mini batches. If omitted, any number of file failures is allowed without terminating the job.  | | `-1` |
 | `settings.logging_level` | string | The log verbosity level. | `warning`, `info`, `debug` | `info` |
 | `settings.mini_batch_size` | integer | The number of files the `code_configuration.scoring_script` can process in one `run()` call. | | `10` |
 | `settings.retry_settings` | object | Retry settings for scoring each mini batch. | | |
@@ -76,7 +76,7 @@ The `az ml batch-deployment` commands can be used for managing Azure Machine Lea
 
 ## Examples
 
-Examples are available in the [examples GitHub repository](https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/batch). Several are shown below.
+Examples are available in the [examples GitHub repository](https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/batch).
 
 ## YAML: MLflow model deployment