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As an ML solutions architecture practitioner, I often receive requests for guidance on designing data management platforms for ML workloads. Although data management platform architecture is typically treated as a separate technical discipline, it plays a crucial role in ML workloads. To create a comprehensive ML platform, ML solutions architects must understand the essential data architecture considerations for machine learning and be familiar with the technical design of a data management platform that caters to the needs of data scientists and automated ML pipelines. In this chapter, we will explore the intersection of data management and ML, discussing key considerations for designing a data management platform specifically tailored for ML. We will delve into the core architecture components of such a platform and examine relevant AWS technologies and services that can be used to build it.
