guinik/Kubernetes-ML-Microservices

This is a teaching/demonstration project (4 days old, 1 star, 0 forks, no velocity) that combines well-established patterns: FastAPI microservices, Kubernetes orchestration (HPA, PodDisruptionBudgets, rolling updates), Terraform IaC, and Groq LLM serving. Each component is commodity technology with no novel combination. The project serves as a reference implementation or tutorial for deploying ML services on Kubernetes+EKS, but introduces no new techniques, architectural insights, or domain-specific innovation. Production-grade maturity is claimed but not evidenced by adoption or battle-testing. The platform domination risk is high because AWS, Google Cloud, and Kubernetes ecosystem maintainers (including AWS's own managed offerings) already provide equivalent or superior infrastructure, and the project adds nothing that justifies building around it rather than using platform-native solutions (e.g., AWS SageMaker, Google Vertex AI, or open-source Seldon/KServe, which are production-grade alternatives). Market consolidation risk is low only because there is no market—this is an educational artifact, not a commercial product competing with established MLOps platforms. Displacement is imminent (6 months) because the project has no defensible position: it's a tutorial without users, novel insights, or community lock-in.