A Practical Guide to Building a Vision AI Model Serving Pipeline with AWS CDK

This article is based on a VAIaaS (Vision AI as a Service) project we developed for production deployment. I’ll share our experience building a Vision AI model serving pipeline using a combination of CDK, API Gateway, Lambda, and SageMaker. Introduction Developing a Vision AI model and deploying that model as an actual service are two entirely different challenges. Even a model that achieves high accuracy in a research environment faces numerous engineering challenges when transitioning it into a stable and scalable API service. From infrastructure provisioning, automated model deployment, and request processing pipeline design to cost optimization and operational monitoring—a unified DevOps framework is essential to manage all of these aspects systematically. When using AWS as your cloud infrastructure, AWS CDK is an excellent choice for this purpose. In this article, I’ll share an architecture and CDK examples for serving models—specifically Vision models—on