CASE STUDY / AI BOX INFRASTRUCTURE & MLOPS
AI infrastructure & MLOps
Infrastructure for ML models and service APIs, with CPU/GPU scheduling, network isolation, and resilience testing.
01 / OVERVIEW
Project overview.
An AI infrastructure project covering deployment configurations for ML models and service APIs, resource scheduling, and network isolation. The public source summary does not specify an employer or project dates.
02 / ARCHITECTURE
How it works.
Kubernetes and Docker supported model and API workloads, with CPU/GPU resource scheduling and network isolation. Load testing and chaos engineering were used to evaluate resilience and failover behavior under production workloads.
03 / FEATURES
What it does.
- Built infrastructure with CPU/GPU resource scheduling.
- Configured network isolation for workloads.
- Created deployment configurations for ML models and service APIs.
- Used load testing and chaos engineering to evaluate resilience and failover behavior.
04 / LESSONS & LIMITATIONS
Learning through building.
The description is sourced from my public GitHub profile. Dates, employer attribution, internal configuration, and benchmark results have not been supplied, so they are omitted. The illustration is a stack overview, not a production architecture diagram.