Udemy – Complete FASTAPI Banking: with AI/ML Bank Fraud Detection 2025-7

During this course, participants will learn how to design a robust banking API architecture using domain-centric design principles. They will learn how to implement secure user authentication using JWT, OTP verification, and request rate limiting. Creating a transaction processing process with currency conversion and fraud detection are other important topics of this course. Participants will also learn how to build a machine learning pipeline for real-time transaction risk analysis. Deploying with Docker Compose and managing traffic with Traefik, scaling the application using asynchronous Celery, monitoring the system using comprehensive logging with Loguru, training, evaluating, and deploying ML models with MLflow, and working with PostgreSQL using SQLModel and Alembic for database migrations are among the skills covered in this course.

What you will learn

• How to integrate Docker with Celery, Redis, RabbitMQ, MLflow, and FastAPI.
• How to use scikit-learn, NumPy, and Pandas for machine learning and build a transaction analysis and fraud detection system.
• How to use MLflow to create machine learning training pipelines and manage the model lifecycle.
• How to use reverse proxies and load balancing with Traefik.
• How to manage multiple Docker containers with Portainer in development and production environments.
• How to use Loguru for comprehensive logging.
• How to use Redis, RabbitMQ, and Celery to process machine learning tasks in the background.

This course is suitable for people who:

• Python developers interested in building fintech APIs.
• Intermediate Python developers with at least one year of experience (more experience is an advantage).
• Intermediate Python developers who are interested in applying machine learning to real-world projects.

Installation Guide

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Subtitles: English

Quality: 720p

Previous Title:

FastAPI, AI & Machine Learning for Bank Fraud Detection

Changes:

Version 2025/5 compared to 2025/4 has increased by 56 lessons and 4 hours and 40 minutes in duration. English subtitles were also added to the course.

Version 2025/7 compared to 2025/5 has increased by 78 lessons and 9 hours and 1 minutes in duration.