Chapter 8: Choosing Real-Time versus Batch Scoring
As you have experienced in the previous chapters, training AutoML models is simple and straightforward. Whether you choose to train a model using the Azure Machine Learning Studio (AMLS) GUI or code an AutoML solution in Python using Jupyter, you can build highly accurate machine learning (ML) models in minutes. However, you still need to learn how to deploy them. In Azure, there are two main ways you can deploy a previously trained ML model to score new data: real-time and batch.
In this chapter, you will begin by learning what a batch scoring solution is, when to use it, and when it makes sense to retrain batch models. Continuing, you will learn what a real-time scoring solution is, when to use it, and when it makes sense to retrain real-time models. Finally, you will conclude by reading a variety of different scenarios and determining which type of scoring you should use. All scenarios are based on common problems faced by real...
