ML Model Deployment — REST API, Docker & Cloud Infrastructure

I will deploy your trained ML model as a production-ready REST API — covering FastAPI endpoint development, input validation and preprocessing, Docker containerisation, cloud deployment with auto-scaling, health check endpoints, request logging, and a CI/CD pipeline for safe model version updates. An ML model that lives in a Jupyter notebook delivers zero business value — deployment to a production serving infrastructure with proper reliability, scalability, and update management is what converts an ML experiment into an ML asset that generates value continuously.

The deployment covers FastAPI inference endpoint with input validation, preprocessing pipeline integration, Dockerfile with multi-stage build, cloud deployment (AWS, GCP, or Azure) with auto-scaling configuration, health and readiness endpoints, structured request/response logging, and a GitHub Actions CI/CD pipeline for model version deployment.