Chapter 9: Building an Enterprise ML Architecture with AWS ML Services

To support a large number of fast-moving machine learning (ML) initiatives, many organizations often decide to build enterprise ML platforms capable of supporting the full ML life cycle, as well as a wide range of usage patterns, which also needs to be automated and scalable. As a practitioner, I have often been asked to provide architecture guidance on how to build enterprise ML platforms. In this chapter, we will discuss the core requirements for enterprise ML platform design and implementation. We will cover topics such as workflow automation, infrastructure scalability, and system monitoring. You will learn about architecture patterns for building technology solutions that automate the end-to-end ML workflow and deployment at scale. We will also dive deep into other core enterprise ML architecture components such as model training, model hosting, the feature store, and the model registry at enterprise scale...