What main difficulties and limitations do you see when integrating AI scenarios into existing backend architecture, and how do you approach solving them?

Main challenges when integrating AI scenarios into existing backend architecture:

• Performance and scalability: AI models often require significant CPU/GPU resources and memory, which can lead to delays and server load.
• Compatibility and integration: The existing architecture may not be ready to work with ML frameworks or services, requiring adapters or microservices.
• Data processing: AI needs high-quality and structured data, often requiring additional preprocessing and validation.
• Model updates: A mechanism for updating and monitoring models without service downtime must be provided.
• Security and confidentiality: Working with personal data requires compliance with norms and protection.

Approaches to solution:

• Moving AI logic to separate microservices or using specialized AI platforms.
• Caching results and asynchronous processing to reduce delays.
• Using queues and events for integration with the main backend.
• Automating CI/CD for models and monitoring prediction quality.
• Implementing abstraction layers to isolate AI components from the main business logic.