Introduction
Imagine you trained a model to predict the product sales. The model has been trained and tracked in Azure Machine Learning. Every month, you want to use the model to forecast the sales for the upcoming month.
In many production scenarios, long-running tasks that deal with large amounts of data are performed as batch operations. In machine learning, batch inferencing is used to asynchronously apply a predictive model to multiple cases and write the results to a file or database.
In Azure Machine Learning, you can implement batch inferencing solutions by deploying a model to a batch endpoint.
Learning objectives
In this module, you'll learn how to:
- Create a batch endpoint.
- Deploy your MLflow model to a batch endpoint.
- Deploy a custom model to a batch endpoint.
- Invoke batch endpoints.