# tag
6 hands-on guides tagged DevOps — real projects, real infrastructure, zero fluff.
The finale: assemble the whole series — FastAPI, Docker, GitHub Actions, ECR, Terraform, EKS, MLflow, Prometheus/Grafana, and drift-triggered retraining — into one platform, and trace a change from git push to a promoted model serving traffic. Part 11 of the MLOps series.
A model can fail with no code change at all: the world moves on and your frozen model goes stale. Detect data and concept drift with a KS test, and build a retraining pipeline that only ships a new model if it beats the current one. Part 10 of the MLOps series.
Stop building infrastructure by hand. Provision the VPC, EKS cluster, ECR, S3, and the GitHub OIDC role with Terraform — reproducible, reviewable, and easy to destroy on demand. Part 8 of the MLOps series.
Take the inference container to a real cluster: Deployments, Services, Ingress, ConfigMaps, Secrets, and an HPA — with the readiness probe we've been building since part two finally doing its job. Part 7 of the MLOps series.
Automate the gate: test the model API, build the image, scan it for CVEs with Trivy, and push to Amazon ECR — with OIDC instead of long-lived keys and the git SHA as an immutable image tag. Part 4 of the MLOps series.
MLOps explained for DevOps engineers — what it is, why plain DevOps isn't enough for ML, and a hands-on look at training vs inference using a real scikit-learn model. Part 1 of an 11-part series.
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