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Your AI system is hard to maintain. Updates take too long. Scaling is expensive. New data sources cause errors. You need a new architecture.
Follow these 5 steps to move to a modular system.
- Map your capabilities
- List your AI functions.
- Map how they depend on each other.
- Find modules with few ties.
- Extract these first.
- Define contracts
- Decide how modules talk.
- Use OpenAPI or gRPC.
- This lets you swap models without breaking the system.
- Manage state
- Keep storage separate.
- Store model files in object storage.
- Put settings in config tools.
- Use caches for fast data.
- Deploy and scale
- Put each module in a container.
- Use Kubernetes for scaling.
- Scale modules based on load.
- Keep training and inference separate.
- Monitor everything
- Add health checks.
- Track latency and errors.
- Use distributed tracing.
- Find bugs fast.
Modular AI scales with your needs. Start with one small module. Prove it works. Then grow.
Source: https://dev.to/jasperstewart/how-to-implement-modular-ai-integration-in-5-practical-steps-532d
Optional learning community: https://t.me/GyaanSetuAi