https://stacksimplify.com/tags/model-deployment/Recent content in Model Deployment on StackSimplify | DevOps & Cloud Education by Kalyan ReddyHugo -- gohugo.ioen-usTue, 14 Apr 2026 00:00:00 +0000https://stacksimplify.com/blog/5-levels-ml-deployment/Tue, 14 Apr 2026 00:00:00 +0000https://stacksimplify.com/blog/5-levels-ml-deployment/You deploy containers to Kubernetes every day. But how do you deploy ML models? There are 5 levels. Each adds production capabilities. Here’s the progression. The 5 Levels Level Pattern DevOps Equivalent When to Use L1 Baked Image Static binary in container Learning, simple models L2 MLflow Dynamic Config from external store Versioned, no rebuild L3 KServe Predictor Deployment + HPA + Ingress Scalable, zero downtime L4 KServe Transformer Sidecar pattern Modular, independent scaling L5 KServe Explainer Audit logging Compliance, GDPR Level 1: Baked Image Model baked into the Docker image at build time.https://stacksimplify.com/blog/ml-deployment-checklist/Tue, 14 Apr 2026 00:00:00 +0000https://stacksimplify.com/blog/ml-deployment-checklist/A data scientist hands you a model.pkl and says “deploy this.” What do you ask? Most engineers jump straight to containers and endpoints. But the questions that save you at 2 AM are the ones you ask before deployment, not during an incident. The Checklist # Question Why It Matters 1 What input will break it? Models return garbage confidently on bad input 2 What’s the rollback plan? “Redeploy the old one” is not a plan 3 How do we know it’s broken?