Designing an MLOps architecture for AI services
Implementing custom AI service models requires a data engineering, model training, and model deployment pipeline. This process is similar to the process of building, training, and deploying models using an ML platform. As such, we can also adopt MLOps practice for AI services when running them at scale.
Fundamentally, MLOps for AI services intends to deliver similar benefits as MLOps for the ML platform, including process consistency, tooling reusability, reproducibility, delivery scalability, and auditability. Architecturally, we can implement a similar MLOps pattern for AI services.
AWS account setup strategy for AI services and MLOps
To isolate the different environments, we can adopt a multi-account strategy for configuring the MLOps environment for AI services. The following diagram illustrates a design pattern for a multi-account AWS environment. Depending on your organizational requirements for separation of duties...
