How to make AI run without changing the system structure

Deploying AI without changing the system structure typically involves using non-invasive integration methods. This is feasible primarily by implementing AI as a distinct external service.

Key approaches include leveraging APIs for communication, using containerized deployment (e.g., Docker, Kubernetes), employing middleware or message queues, and adopting a microservices architecture. These methods allow the AI model to operate independently, minimizing dependencies on the core system's internal logic. Ensuring clear data input/output interfaces and robust security protocols between the systems is critical. The core system's stability remains largely unaffected.

The primary implementation steps are: 1) Develop the AI model as a standalone service. 2) Containerize it for easy deployment. 3) Define well-documented APIs for the core system to send data and receive predictions/results. 4) Deploy this AI service on dedicated infrastructure or cloud platforms. 5) Integrate the core system with the AI service via API calls. This approach enables rapid AI adoption, maintains legacy system integrity, and allows for independent scaling and updating of the AI component.