AI Model CI/CD Pipeline (MLOps)
Built a complete MLOps CI/CD pipeline for deploying LLM models on AWS EKS with canary deployments and automated rollback. The pipeline features a three-stage workflow: CI (linting, unit tests, model evaluation against quality thresholds for latency, throughput, and accuracy), staging deployment with smoke tests, and production canary rollout (10% → monitor → promote or rollback). Infrastructure is managed via Terraform (ECR, S3 with lifecycle policies, IAM with GitHub OIDC for keyless auth). Kubernetes manifests use Kustomize overlays for environment-specific configs. An automated model evaluation framework compares each deployment against baseline quality scores, gating production releases on p95 latency < 5s, throughput > 50 tokens/sec, and error rate < 1%. Includes a version tracker storing deployment history in S3 for audit and rollback.
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