What you'll learn:
- You will learn how to integrate Docker with Celery, Redis,RabbitMQ, FlowermMLFlow and FastAPI
- You will learn how to use scikit learn,numpy and pandas for machine learning, to create a transaction analysis and Fraud detection system
- You will learn how to use mlflow to create machine learning training pipelines and lifecycle management
- You will learn how to use Reverse Proxies and load balancing with TRAEFIK
- You will learn how manage multiple Docker containers with Portainer in development and in Production
- You will learn how to use Loguru for comprehensive Logging
- You will learn how to use Redis,RabbitMQ and celery for background machine learning task processing.
Welcome to this comprehensive course on building a banking API with FastAPI with an AI-powered/machine learning transaction analysis and fraud detection system. This course goes beyond basic API development to show you how to architect a complete banking system that's production-ready, secure, and scalable.
What Makes This Course Unique:
Learn to build a real-world banking system with FastAPI and SQLModel
Implement AI/ML-powered fraud detection using MLflow and scikit-learn
Master containerization with Docker
Master reverse proxying and load balancing with Traefik
Handle high-volume transactions with Celery, Redis, and RabbitMQ
Secure your API with industry-standard authentication practices
You'll Learn How To:
✓ Design a robust banking API architecture with domain-driven design principles
✓ Implement secure user authentication with JWT, OTP verification, and rate limiting
✓ Create transaction processing with currency conversions and fraud detection
✓ Build a machine learning pipeline for real-time transaction risk analysis
✓ Deploy with Docker Compose and manage traffic with Traefik
✓ Scale your application using asynchronous Celery workers
✓ Monitor your system with comprehensive logging using Loguru
✓ Train, evaluate, and deploy ML models with MLflow
✓ Work with PostgreSQL using SQLModel and Alembic for migrations
Key Features in This Project:
Core Banking Functionality: Account creation, transfers, deposits, withdrawals, statements
Virtual Card Management: Card creation, activation, blocking, and top-ups
User Management: Profiles, Next of Kin information, KYC implementation
AI/ML-Powered Fraud Detection: ML-based transaction analysis and fraud detection
Background Processing: Email notifications, PDF generation, and ML training
Advanced Deployment: Container orchestration, reverse proxying, and high availability
ML Ops: Model training, evaluation, deployment, and monitoring
This course is perfect For:
• Backend developers with at least 1 year of experience, looking to build secure fintech solutions.
• Tech leads planning to architect fintech solutions.
By the end of this course, you'll have built a production-ready banking system with AI capabilities that you can showcase in your portfolio or implement in real-world projects.
Technologies You'll Master:
FastAPI & SQLModel: For building high-performance, type-safe APIs
Docker & Traefik: For containerization and intelligent request routing
Celery & RabbitMQ: For distributed task processing
PostgreSQL & Alembic: For robust data storage and schema migrations
Scikit-learn: For machine learning.
MLflow: For managing the machine learning lifecycle
Pydantic V2: For data validation and settings management
JWT & OTP: For secure authentication flows
Cloudinary: For handling image uploads
Rate Limiting: For API protection against abuse
No more basic tutorials - let's build something real!
