Update how-to-batch-scoring-script.md

Author: santiagxf

articles/machine-learning/how-to-batch-scoring-script.md

@@ -17,10 +17,10 @@ ms.custom: how-to
 
 [!INCLUDE [cli v2](../../includes/machine-learning-dev-v2.md)]
 
-Batch endpoints allow you to deploy models to perform inference at scale. Because how inference should be executed varies from model's format, model's type and use case, batch endpoints require a scoring script (also known as batch driver script) to indicate the deployment how to use the model over the provided data. In this article you will learn how to use scoring scripts in different scenarios and their best practices.
+Batch endpoints allow you to deploy models to perform long-running inference at scale. To indicate how batch endpoints should use your model over the input data to create predictions, you need to create and specify a scoring script (also known as batch driver script). In this article, you will learn how to use scoring scripts in different scenarios and their best practices.
 
 > [!TIP]
-> MLflow models don't require a scoring script as it is autogenerated for you. For more details about how batch endpoints work with MLflow models, see the dedicated tutorial [Using MLflow models in batch deployments](how-to-mlflow-batch.md). Notice that this feature doesn't prevent you from writing an specific scoring script for MLflow models as explained at [Using MLflow models with a scoring script](how-to-mlflow-batch.md#customizing-mlflow-models-deployments-with-a-scoring-script).
+> MLflow models don't require a scoring script as it is autogenerated for you. For more details about how batch endpoints work with MLflow models, see the dedicated tutorial [Using MLflow models in batch deployments](how-to-mlflow-batch.md). If you want to change the default inference routine, write an scoring script for your MLflow models as explained at [Using MLflow models with a scoring script](how-to-mlflow-batch.md#customizing-mlflow-models-deployments-with-a-scoring-script).
 
 > [!WARNING]
 > If you are deploying an Automated ML model under a batch endpoint, notice that the scoring script that Automated ML provides only works for Online Endpoints and it is not designed for batch execution. Please follow this guideline to learn how to create one depending on what your model does.
@@ -33,7 +33,7 @@ The scoring script must contain two methods:
 
 #### The `init` method
 
-Use the `init()` method for any costly or common preparation. For example, use it to load the model into memory. This function will be called once at the beginning of the entire batch job. Your model's files will be available in a path determined by the environment variable `AZUREML_MODEL_DIR`. Notice that depending on how your model was registered, its files may be contained in a folder (in the following example, the model has several files in a folder named `model`). See [how you can find out what's the folder used by your model](#using-models-that-are-folders).
+Use the `init()` method for any costly or common preparation. For example, use it to load the model into memory. This function is called once at the beginning of the entire batch job. Your model's files are available in a path determined by the environment variable `AZUREML_MODEL_DIR`. Notice that depending on how your model was registered, its files may be contained in a folder (in the following example, the model has several files in a folder named `model`). See [how you can find out what's the folder used by your model](#using-models-that-are-folders).
 
 ```python
 def init():
@@ -51,7 +51,7 @@ Notice that in this example we are placing the model in a global variable `model
 
 #### The `run` method
 
-Use the `run(mini_batch: List[str]) -> Union[List[Any], pandas.DataFrame]` method to perform the scoring of each mini-batch generated by the batch deployment. Such method will be called once per each `mini_batch` generated for your input data. Batch deployments read data in batches accordingly to how the deployment is configured.
+Use the `run(mini_batch: List[str]) -> Union[List[Any], pandas.DataFrame]` method to perform the scoring of each mini-batch generated by the batch deployment. Such method is called once per each `mini_batch` generated for your input data. Batch deployments read data in batches accordingly to how the deployment is configured.
 
 ```python
 import pandas as pd
@@ -66,14 +66,14 @@ def run(mini_batch: List[str]) -> Union[List[Any], pd.DataFrame]:
     return pd.DataFrame(results)
 ```
 
-The method receives a list of file paths as a parameter (`mini_batch`). You can use this list to either iterate over each file and process it one by one, or to read the entire batch and process it at once. The best option will depend on your compute memory and the throughput you need to achieve. For an example of how to read entire batches of data at once see [High throughput deployments](how-to-image-processing-batch.md#high-throughput-deployments).
+The method receives a list of file paths as a parameter (`mini_batch`). You can use this list to either iterate over each file and process it one by one, or to read the entire batch and process it at once. The best option depends on your compute memory and the throughput you need to achieve. For an example of how to read entire batches of data at once see [High throughput deployments](how-to-image-processing-batch.md#high-throughput-deployments).
 
 > [!NOTE]
 > __How is work distributed?__
 > 
 > Batch deployments distribute work at the file level, which means that a folder containing 100 files with mini-batches of 10 files will generate 10 batches of 10 files each. Notice that this will happen regardless of the size of the files involved. If your files are too big to be processed in large mini-batches we suggest to either split the files in smaller files to achieve a higher level of parallelism or to decrease the number of files per mini-batch. At this moment, batch deployment can't account for skews in the file's size distribution.
 
-The `run()` method should return a Pandas `DataFrame` or an array/list. Each returned output element indicates one successful run of an input element in the input `mini_batch`. For file datasets, each row/element will represent a single file processed. For a tabular dataset, each row/element will represent a row in a processed file.
+The `run()` method should return a Pandas `DataFrame` or an array/list. Each returned output element indicates one successful run of an input element in the input `mini_batch`. For file datasets, each row/element represents a single file processed. For a tabular dataset, each row/element represents a row in a processed file.
 
 > [!IMPORTANT]
 > __How to write predictions?__
@@ -85,11 +85,11 @@ The `run()` method should return a Pandas `DataFrame` or an array/list. Each ret
 > [!WARNING]
 > Do not not output complex data types (or lists of complex data types) in the `run` function. Those outputs will be transformed to string and they will be hard to read.
 
-The resulting DataFrame or array is appended to the output file indicated. There's no requirement on the cardinality of the results (1 file can generate 1 or many rows/elements in the output). All elements in the result DataFrame or array will be written to the output file as-is (considering the `output_action` isn't `summary_only`).
+The resulting DataFrame or array is appended to the output file indicated. There's no requirement on the cardinality of the results (1 file can generate 1 or many rows/elements in the output). All elements in the result DataFrame or array are written to the output file as-is (considering the `output_action` isn't `summary_only`).
 
 #### Python packages for scoring
 
-Any library that your scoring script requires to run needs to be indicated in the environment where your batch deployment runs. As for scoring scripts, environments are indicated per deployment. Usually, you will indicate your requirements using a `conda.yml` dependencies file which may look as follows:
+Any library that your scoring script requires to run needs to be indicated in the environment where your batch deployment runs. As for scoring scripts, environments are indicated per deployment. Usually, you indicate your requirements using a `conda.yml` dependencies file, which may look as follows:
 
 __mnist/environment/conda.yml__
 
@@ -99,7 +99,7 @@ Refer to [Create a batch deployment](how-to-use-batch-endpoint.md#create-a-batch
 
 ## Writing predictions in a different way
 
-By default, the batch deployment will write the model's predictions in a single file as indicated in the deployment. However, there are some cases where you need to write the predictions in multiple files. For instance, if the input data is partitioned, you typically would want to generate your output partitioned too. On those cases you can [Customize outputs in batch deployments](how-to-deploy-model-custom-output.md) to indicate:
+By default, the batch deployment writes the model's predictions in a single file as indicated in the deployment. However, there are some cases where you need to write the predictions in multiple files. For instance, if the input data is partitioned, you typically would want to generate your output partitioned too. On those cases you can [Customize outputs in batch deployments](how-to-deploy-model-custom-output.md) to indicate:
 
 > [!div class="checklist"]
 > * The file format used (CSV, parquet, json, etc).
@@ -123,13 +123,13 @@ When writing scoring scripts that work with big amounts of data, you need to tak
 * The memory footprint of the model when running over the input data.
 * The available memory in your compute.
 
-Batch deployments distribute work at the file level, which means that a folder containing 100 files with mini-batches of 10 files will generate 10 batches of 10 files each. Notice that this will happen regardless of the size of the files involved. If your files are too big to be processed in large mini-batches we suggest to either split the files in smaller files to achieve a higher level of parallelism or to decrease the number of files per mini-batch. At this moment, batch deployment can't account for skews in the file's size distribution.
+Batch deployments distribute work at the file level, which means that a folder containing 100 files with mini-batches of 10 files will generate 10 batches of 10 files each (regardless of the size of the files involved). If your files are too big to be processed in large mini-batches, we suggest to either split the files in smaller files to achieve a higher level of parallelism or to decrease the number of files per mini-batch. At this moment, batch deployment can't account for skews in the file's size distribution.
 
 ### Relationship between the degree of parallelism and the scoring script
 
 Your deployment configuration controls the size of each mini-batch and the number of workers on each node. Take into account them when deciding if you want to read the entire mini-batch to perform inference, or if you want to run inference file by file, or row by row (for tabular). See [Running inference at the mini-batch, file or the row level](#running-inference-at-the-mini-batch-file-or-the-row-level) to see the different approaches.
 
-When running multiple workers on the same instance, take into account that memory will be shared across all the workers. Usually, increasing the number of workers per node should be accompanied by a decrease in the mini-batch size or by a change in the scoring strategy (if data size and compute SKU remains the same).
+When running multiple workers on the same instance, take into account that memory is shared across all the workers. Usually, increasing the number of workers per node should be accompanied by a decrease in the mini-batch size or by a change in the scoring strategy (if data size and compute SKU remains the same).
 
 ### Running inference at the mini-batch, file or the row level
 
@@ -142,7 +142,7 @@ You will typically want to run inference over the batch all at once when you wan
 > [!WARNING]
 > Running inference at the batch level may require having high control over the input data size to be able to correctly account for the memory requirements and avoid out of memory exceptions. Whether you are able or not of loading the entire mini-batch in memory will depend on the size of the mini-batch, the size of the instances in the cluster, the number of workers on each node, and the size of the mini-batch.
 
-For an example about how to achieve it see [High throughput deployments](how-to-image-processing-batch.md#high-throughput-deployments). This example processes an entire batch of files at a time.
+For an example about how to achieve it, see [High throughput deployments](how-to-image-processing-batch.md#high-throughput-deployments). This example processes an entire batch of files at a time.
 
 #### File level