Architecting batch scoring solutions
Batch inferencing refers to scoring new data points in batches on a recurring time-based schedule. New data is collected over time and subsequently scored, generating new predictions. This is the most common way modern companies use ML models.
In this section, you will learn how to architect a complete, end-to-end batch scoring solution using Azure AutoML-trained models. You will also learn why, and in what situations, you should prioritize batch scoring over real-time scoring solutions.
Understanding the five-step batch scoring process
Each batch scoring solution you make should follow a five-step process. This process begins by training and registering an ML model as you did in the previous chapters using AMLS. Regression, classification, and forecasting models all follow the same pattern. In order, the five steps are as follows:
- Train a model. You can train a model either using the AMLS GUI as you did in Chapter 3, Training...
