Creating a batch and real-time inference pipeline

This section will discuss the two options of deploying an inference pipeline from the designer: batch and real time:

• With batch predictions, you asynchronously score large datasets.
• With real-time prediction, you score a small dataset or a single row in real time.

When you create an inference pipeline, either batch or real time, AzureML takes care of the following things:

• AzureML stores the trained model and all the trained data processing modules as an asset in the asset library under the Datasets category.
• It removes unnecessary modules such as Train Model and Split Data automatically.
• It adds the trained model to the pipeline.

Especially for real-time inference pipelines, AzureML will add a web service input and a web service output in the final pipeline.

Let's start by creating a batch pipeline, something you will do in the next section.

Creating a batch pipeline

In this section...