Model Deployment: From Notebook to Production

Building an ML model is only half the battle. Getting it into production reliably is the other half. This course bridges the gap between data science notebooks and production-grade ML systems.

You will learn to package models with FastAPI, containerize with Docker, deploy to cloud platforms (AWS, GCP, Azure), set up CI/CD pipelines, implement A/B testing, monitor model performance and data drift, and handle scaling challenges.

Projects include deploying a real-time prediction API, setting up a batch inference pipeline, building a model registry, and implementing automated retraining. By the end, you will have the DevOps and MLOps skills that employers increasingly demand from data scientists.