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FastAPI: Build a Banking API that has AI/ML Fraud Detection - AD-TEAM - 06-22-2025
![[Image: b83419c55d34911b1232a0195207b625.jpg]](https://i125.fastpic.org/big/2025/0622/25/b83419c55d34911b1232a0195207b625.jpg)
FastAPI: Build a Banking API that has AI/ML Fraud Detection.
Published 5/2025
Duration: 9h 28m | .MP4 1280x720, 30 fps® | AAC, 44100 Hz, 2ch | 6.66 GB
Genre: eLearning | Language: English
Learn FastAPI, MLFlow, AI/ML, Docker, Celery etc, to build a banking API with transaction fraud protection
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.
Requirements
- This course is NOT for absolute beginners.
- This course is targeted at Python Developers with at least 1 year of web development experience or more
- You should be familiar with the basic concepts surrounding shell scripts, Docker, and FastAPI.
- You should be familiar with concepts surrounding asynchronous python.
Description
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!
Who this course is for:
- Python Developers,curious about building a Fintech API's
- Intermediate Python Developers with at least 1 year of experience, more is better
- Intermediate Python Develpers curious about machine learning applications in real world projects.
More Info
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