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.
