Deploy a model to a batch endpoint

Deploying a Model to a Batch Endpoint in Azure

In Azure, you can deploy a model to a batch endpoint to process large volumes of data for inference in your data science solution. Here’s a step-by-step guide on how to deploy a model to a batch endpoint using Azure Machine Learning:

1. Prepare the Model

To begin, you need to train and validate your model using your preferred data science tools. Once trained, save the model in a format that can be used by Azure Machine Learning, such as pickle, ONNX, or TensorFlow SavedModel. It’s important to properly document the dependencies and packages required to run the model.

2. Set up an Azure Machine Learning Workspace

If you haven’t already, create an Azure Machine Learning workspace. Install the Azure Machine Learning Python SDK and authenticate using your Azure credentials. Create an experiment to track and organize your deployment pipeline.

3. Define the Batch Inference Pipeline

In Azure Machine Learning, use the SDK to define the steps of your batch inference pipeline. This typically includes steps to load the data, preprocess it, and run the model for inference. Use the ParallelRunConfig class to define the compute target, environment, and other configurations for the batch inference pipeline.

4. Create the Batch Endpoint

Use the SDK to create a batch endpoint, which is a managed resource in Azure that can handle the processing of large volumes of data for inference. Specify the model, container image, and other required configurations while creating the batch endpoint. Make sure to allocate sufficient resources to handle the expected throughput and data volume.

5. Run Batch Inference Jobs

Submit a batch inference job using the ParallelRunStep class. Specify the input data, output data, and other required configurations. Monitor the status and progress of the batch inference job using the Azure Machine Learning SDK. Once the batch inference job completes successfully, retrieve the results.

Here’s an example code snippet that demonstrates the deployment of a model to a batch endpoint:

from azureml.core import Environment, Experiment, Workspace
from azureml.core.compute import BatchAiCompute
from azureml.core.model import InferenceConfig, Model
from azureml.pipeline.core import Pipeline
from azureml.pipeline.steps import ParallelRunStep, ParallelRunConfig

# Create the pipeline and submit the batch inference job
pipeline = Pipeline(workspace=workspace, steps=[parallel_run_step])
pipeline_run = experiment.submit(pipeline)


By following these steps and utilizing the Azure Machine Learning service, you can easily deploy your model to a batch endpoint, allowing you to efficiently process large volumes of data for inference in your data science solution on Azure.