ML Model Deployment: Build a Production API with FastAPI

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. The fraud detection project is ideal because it mirrors real production ML systems — it demands low-latency inference, handles high-throughput traffic, requires model versioning (regulations mandate auditability), and needs continuous monitoring for concept drift as fraud patterns evolve. Every concept is demonstrated by extending the deployment pipeline, so learners see their project grow from a local pickle file to a fully automated, monitored, cloud-deployed ML service. By course end, learners will have a complete MLOps pipeline and the skills to deploy any ML model to production with confidence Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.