Need a working, end-to-end MLOps example that shows how a classification model is taken from code to a live, consumable service on AWS. The spotlight is firmly on Model deployment, but I still want the repository organised so the other pillars of MLOps (source control, automated testing, CI/CD, reproducible environments, basic monitoring hooks) are visible and ready to be extended. Project outline • Train at least two variants of an SGD-based classifier locally (Sklearn is fine) and version them. • Containerise the chosen model, push the image to ECR, and deploy it behind an AWS service that can expose a REST endpoint (SageMaker, ECS or EKS—use what makes the most sense). • Script all cloud resources with IaC (Terraform or CloudFormation). • Wire a CI/CD pipeline (GitHub Actions, CodePipeline, or similar) so every commit runs tests, builds, and promotes the container through staging to production with blue-green or canary deployment. • Add lightweight logging/metrics so I can verify the model’s health post-launch; a simple CloudWatch dashboard is enough for now. Deliverables 1. Public or private Git repo containing code, Dockerfile, IaC scripts, and pipeline configuration. 2. Step-by-step README that lets me reproduce the full flow in my own AWS account. 3. Short demo video or screenshots confirming the endpoint is live and responding. Acceptance criteria • `git clone`, one-command bootstrap, and the pipeline finishes without manual tweaks. • Hitting the final endpoint returns a prediction for a sample record in under 500 ms. • I can swap in a new SGD model version and see the pipeline redeploy automatically. Please keep the solution tidy, well-documented, and focused on deployment; deeper model-training experimentation or advanced monitoring is welcome but not required for this milestone.