Fraud Detection Machine learning pipeline walkthrough.(FastAPI, MLflow, Feature Engineering etc )

Description

In this video I walk through the full machine learning pipeline that powers real-time fraud detection inside the NextGenBank FastAPI banking system — from raw transaction data all the way to deployed model inference, human review, and automated retraining. We move past simple rule-based fraud checks and design an adaptive, data-driven pipeline using a Gradient Boosting Classifier, with MLflow handling experiment tracking, model versioning, and deployment. If you've ever wanted to see how a production ML system is actually wired into a backend API — not just a Jupyter notebook demo — this one's for you. 🎓 Full hands-on course on Udemy: 👉 [https://www.udemy.com/course/fastapi-banking-with-ai-ml-fraud-detection/?referralCode=6AF6C11C157E9FE26090] 💻 Source code on GitHub: 👉 https://github.com/API-Imperfect/nextgenbank-fastapi 📺 Watch the full system architecture overview first: 👉 [https://youtu.be/567XMng-0GA] 🧠 What you'll learn How to structure an end-to-end ML pipeline for a real backend system Feature engineering strategies for fraud detection (velocity, time-based, behavioral) Why Gradient Boosting is a strong baseline for tabular fraud problems How to evaluate fraud models the right way (AUC, precision, recall, F1) How MLflow ties experiments, model versions, and deployments together How to design a feedback loop with human review for continuous improvement 🧰 Tech & concepts covered Gradient Boosting Classifier Feature engineering & feature importance Train/test split & model evaluation metrics MLflow tracking server, model registry & deployment Real-time model inference & risk scoring Human-in-the-loop review & scheduled retraining Integration with FastAPI, Celery, Redis & RabbitMQ 👨‍💻 Who this is for Backend engineers, Python developers, and ML-curious builders who want to see how a real fraud detection system is architected inside a production API — not isolated as a notebook experiment. 🎓 Full hands-on course on Udemy: 👉 [https://www.udemy.com/course/fastapi-banking-with-ai-ml-fraud-detection/?referralCode=6AF6C11C157E9FE26090] 💻 Source code on GitHub: 👉 https://github.com/API-Imperfect/nextgenbank-fastapi #MachineLearning #FraudDetection #MLOps #MLflow #GradientBoosting #FastAPI #Python #SystemDesign #DataScience #Backend #Fintech #AI