Enhance your machine learning development by using a modular architecture with Amazon SageMaker projects

One of the main challenges in a machine learning (ML) project implementation is the variety and high number of development artifacts and tools used. This includes code in notebooks, modules for data processing and transformation, environment configuration, inference pipeline, and orchestration code. In production workloads, the ML model created within your development framework is almost never the end of the work, but is a part of a larger application or workflow. Another challenge is the varied nature of ML development activities performed by different user roles. For example, the DevOps engineer develops infrastructure components, such as CI/CD automation, builds production inference pipelines, and configures security and networking.