Summary
You now have a firm understanding of batch and real-time inferencing, and when to use which type of scoring solution. This is important, as even seasoned data scientists occasionally make mistakes when designing end-to-end ML solutions.
Furthermore, most ML courses focus on training models instead of deploying them, but to be an effective data scientist, you must be proficient at both. In the upcoming chapters, you will learn how to code each of these inferencing methods in AMLS.
In Chapter 9, Implementing a Batch Scoring Solution, you will learn step by step how to use the ML models you've already built in batch scoring scenarios. You will create ML pipelines in AMLS and learn how to schedule them to run on a timer. This will allow you to easily productionalize your ML models and become a valuable asset to your company or organization.