Invoke the batch endpoint to start a batch scoring job

To perform batch scoring on Azure, you can utilize the batch endpoint in Azure Machine Learning service. Batch scoring allows you to apply your trained models to large datasets in a distributed and parallel manner, maximizing efficiency. In this article, we will explore how to invoke the batch endpoint to start a batch scoring job as part of designing and implementing a data science solution on Azure.

Prerequisites

Before proceeding, ensure that you have completed the following prerequisites:

• Create an Azure Machine Learning workspace
• Create a compute target
• Deploy a model

If you haven’t completed these prerequisites, refer to the relevant Azure documentation for detailed guidance.

Step 1: Set up Authentication

To authenticate your requests, you need to obtain an access token. You can do this by using the Azure Active Directory authentication library (ADAL) to authenticate against Azure Active Directory and obtain an access token. The following code snippet demonstrates how to obtain the access token programmatically using Python:

python
from azureml.core.authentication import AzureCliAuthentication

# Use Azure CLI authentication
auth = AzureCliAuthentication()

# Obtain access token
context = auth.get_authentication_context()
access_token = context.acquire_token(“https://management.core.windows.net/”)

# Use the access token for subsequent requests

Step 2: Prepare the Scoring Script

Create a Python script that defines the scoring logic for your batch scoring job. This script will be executed against each input data record in the batch dataset. Ensure that the script follows the syntax and logic required for your specific use case. Below is an example of a scoring script that imports the necessary modules and performs scoring using a trained model:

python
import joblib
import pandas as pd

def init():
# Load the trained model
global model
model_path = ‘model.pkl’
model = joblib.load(model_path)

def run(input_data):
# Convert input data to pandas DataFrame
data = pd.DataFrame(input_data)

# Perform scoring using the trained model
results = model.predict(data)

# Return the scoring results
return results.tolist()

Step 3: Create a Scoring Environment

To execute the scoring script, you need to define a scoring environment that includes all the necessary dependencies. This can be achieved by creating a Conda environment specification file (environment.yml). The following code snippet demonstrates an example environment.yml file:

yaml
name: scoring_environment
dependencies:
– python=3.8
– pip:
– azureml-core
– azureml-defaults
– pandas
– scikit-learn
– joblib

Step 4: Create a Batch Scoring Job

To create a batch scoring job, you need to define the details such as the input and output dataset, the scoring script, and the scoring environment. The following code snippet illustrates how to create a batch scoring job using Python:

python
from azureml.core import Dataset, Environment, ScriptRunConfig, Workspace

# Load the Azure Machine Learning workspace
workspace = Workspace.from_config()

# Get the input dataset
input_dataset = Dataset.get_by_name(workspace, name=’input_dataset’)

# Get the output dataset
output_dataset = Dataset.get_by_name(workspace, name=’output_dataset’)

# Define the scoring environment
environment = Environment.from_conda_specification(‘scoring_env’, ‘environment.yml’)

# Define the scoring script run configuration
script_run_config = ScriptRunConfig(
source_directory=’path_to_scripts’,
script=’score.py’,
arguments=[‘–input’, input_dataset.as_named_input(‘input_data’), ‘–output’, output_dataset.as_named_output(‘output_data’)],
compute_target=’compute_target’,
environment=environment
)

# Submit the batch scoring job
run = experiment.submit(script_run_config)

Make sure to replace the necessary placeholders with your own values, such as the dataset names, script paths, and compute target.

Step 5: Monitor the Scoring Job

Once the job is submitted, you can monitor its progress using the Azure Machine Learning Studio or programmatically using the Azure Machine Learning SDK. This allows you to track the status, view logs, and retrieve the output results once the job is completed.

Conclusion

In this article, we learned how to invoke the batch endpoint to start a batch scoring job on Azure. By leveraging batch scoring, you can efficiently apply your trained models to large datasets. Remember to follow the steps outlined in this article and refer to the Azure documentation for additional details and advanced configurations.