Personal / lab
Local AI as a layered platform.
A lab architecture for learning how model serving, retrieval, orchestration, and observability fit together under local control.
Personal / lab:This independent learning project is not presented as client or employer work.
Challenge
A useful private AI environment involves more than running a model. Compute, storage, retrieval, interfaces, access, updates, and failure visibility all need explicit boundaries.
Approach
Treat the environment as replaceable layers. Keep model serving independent from user interfaces, put retrieval behind a defined service boundary, and observe requests and resource pressure without exposing prompts or private data unnecessarily.
Architecture
Tools & methods
- Model serving
- Retrieval patterns
- Containerization
- Resource scheduling
- Service health
- Observability
What this demonstrates
Practical platform thinking: isolate change, make dependencies visible, preserve model flexibility, and operate the environment as a system rather than a collection of demos.
Privacy by design. This case study describes patterns and decision boundaries only. It does not publish private addresses, credentials, customer data, employer systems, exact inventory, or operational access details.