平台工程：以 Kubernetes 支援 AI 在生產環境中可靠擴展
Platform Engineering
AI 在生產環境中擴展，關鍵不只在提升模型能力，也在於建構能可靠、大規模提供服務的基礎設施。Kubernetes 和容器編排可部署推理、API 服務、檢索流程、嵌入向量生成及背景處理等元件，並按需求擴展工作負載、管理資源和提升韌性。雲原生架構及編排正成為把 AI 方案由原型推向生產環境的關鍵。
Scaling AI in production is not just about making models more powerful — it's about building the infrastructure to serve them reliably at scale.
The shift from monolithic applications to microservices taught us an important lesson: break complex systems into manageable components that can scale independently.
The same principle applies to AI solutions. An AI application may involve model inference, API services, retrieval pipelines, embedding generation, and background processing. These components often have different resource requirements and scaling patterns.
This is where Kubernetes and container orchestration come in. They help us deploy these components, scale workloads based on demand, manage resources efficiently, and improve resilience.
Just as microservices transformed how we build and scale traditional applications, cloud-native architecture and orchestration are becoming essential to taking AI solutions from prototypes to production.
The next step in AI isn't just building smarter models. It's building systems that can deliver AI reliably, efficiently, and at scale.
來源：DEV Community · dev.to