Definition
In the context of AI engineering, microservices allow different components of an AI pipeline, such as data preprocessing, model inference, and result storage, to be developed, scaled, and maintained independently. This contrasts with monolithic architectures by promoting modularity and resilience. Each service communicates via lightweight protocols like HTTP or gRPC. This approach facilitates continuous integration and deployment, enabling teams to update specific AI models or features without disrupting the entire system, thereby improving agility and fault isolation.
Summary
Microservices is an architectural style that structures an application as a collection of loosely coupled, independently deployable services organized around business capabilities.
Key Concepts
- Loose coupling
- Independent deployment
- Service decomposition
- API gateways
Use Cases
- Scalable AI inference endpoints
- Modular machine learning pipelines
- Resilient recommendation systems