Summary
We started this chapter by providing a general overview of the DL domain and its challenges, as well as the Amazon SageMaker service and its value proposition for DL workloads. Then, we reviewed the core SageMaker capabilities: managed training and managed hosting. We examined the life cycle of a SageMaker training job and real-time inference endpoint. Code snippets demonstrated how to configure and provision SageMaker resources programmatically using its Python SDK. We also looked at other relevant AWS services as we will be using them a lot in the rest of this book. This will help us as we now have a good grounding in their uses and capabilities.
In the next chapter, we will dive deeper into the foundational building blocks of any SageMaker workload: runtime environments (specifically, supported DL frameworks) and containers. SageMaker provides several popular pre-configured runtime environments and containers, but it also allows you to fully customize them via its “BYO container” feature. We will learn when to choose one of these options and how to use them.