Deploying an ML Model with a SageMaker Real-Time Endpoint
Overview Training a model is the easy part. Getting it into production — where it serves real-time predictions reliably — is where most ML projects stall. This project deploys a trained bank churn model as a SageMaker real-time endpoint using a custom Docker inference container, then adds a Lambda function to automate inference whenever new data lands in S3. The stack: FastAPI + uvicorn as the inference server, Docker for packaging, ECR for the registry, SageMaker for hosting, and Lambda + S3 for event-driven automation. ...