Designing an Enterprise ML Architecture with AWS ML Services
Many organizations opt to build enterprise ML platforms to support numerous fast-moving initiatives. These platforms are designed to facilitate the entire ML lifecycle and accommodate various usage patterns, all while emphasizing automation and scalability. As a practitioner, I often get asked to provide architectural guidance for creating such enterprise ML platforms. In this chapter, we will explore the fundamental requirements for designing enterprise ML platforms. We will cover a range of topics, such as workflow automation, infrastructure scalability, and system monitoring.
Throughout the discussion, you will gain insights into architecture patterns that enable the development of technology solutions to automate the end-to-end ML workflow and ensure seamless deployment at a large scale. Additionally, we will delve deep into essential components of enterprise ML architecture, such as model training, model hosting...
