ML Model Deployment: Build a Production API with FastAPI
Coursera · beginner · 5h
This fully hands-on course teaches learners to take machine learning models from Jupyter notebooks to production-grade deployed services using the modern MLOps stack. Using a single progressively built project — deploying a Real-Time Fraud Detection System for a fintech company — learners will master every stage of the ML deployment lifecycle: packaging models with FastAPI, containerizing with Docker, tracking experiments with MLflow, building CI/CD pipelines with GitHub Actions, orchestrating with Docker Compose and Kubernetes, monitoring model performance with Prometheus and Grafana, and detecting data drift in production.
Skills covered
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