Machine learning (ML) has quickly become integral to many businesses. Its rapid adoption across almost every industry is because it is a force multiplier: new data can be learned from and understood as it arrives, while historical data can be revisited with new tools and practices.

Continuous Integration/Continuous Delivery (CI/CD) has become an essential part of modern software development, allowing teams to deliver high-quality code at a faster pace. Teams can either build or buy their CI/CD system. In this blog post, we will compare both options - exploring the advantages and disadvantages of each - and why CircleCI may be the better choice.

There are many benefits of incorporating CI/CD into your ML pipeline, such as automating the deployment of ML models to production at scale. The focus of this article is to illustrate how to integrate AWS SageMaker model training and deployment into CircleCI CI/CD pipelines. The structure of this project is a monorepo containing multiple models. The monorepo approach has advantages over the polyrepo approach, including simplified dependency versioning and security vulnerability management.