Getting Started with ML Model Deployment

Deploying machine learning models can feel overwhelming at first. In this post, I'll walk through a minimal but production-ready setup using FastAPI as the serving layer.

Start by packaging your model with a clear inference interface. Keep preprocessing and postprocessing logic close to the model definition so behavior stays consistent between training and serving.

Containerize the service with Docker, add health checks, and expose a simple /predict endpoint. From there, you can iterate on monitoring, scaling, and CI/CD without rewriting the core API.