Python Backend Development with AI
AI models need to live somewhere real users and systems can reach them. This module builds the production-grade backend engineering skillset — APIs, databases, auth and deployment — that every AI system is served through.
What You Will Learn
A detailed, industry-aligned breakdown of every topic covered in this module.
- Advanced Python and modular application architecture
- FastAPI development and RESTful API design
- PostgreSQL and MongoDB database fundamentals
- Redis and background job queues
- Authentication, authorization and JWT / OAuth
- API security and role-based access control
- Testing and test-driven development
- Docker containerization
- CI/CD with GitHub Actions
- Cloud deployment fundamentals
- Capstone: production-style backend service
Tools You Will Use
Hands-on time with the same tools used in professional AI engineering and production ML workflows.
Python
Core programming language used across every module, from scripting to AI model development.
FastAPI
Modern Python web framework used to build production-grade backend and AI-serving APIs.
PostgreSQL
Relational database used to store structured application and AI system data.
MongoDB
NoSQL document database used for flexible, schema-less application data storage.
Redis
In-memory data store used for caching, background job queues and fast lookups.
Docker
Containerization platform used to package and deploy applications and AI services consistently.
GitHub Actions
CI/CD automation platform used to test, build and deploy code on every change.
Pytest
Python testing framework used to write and run automated unit and integration tests.
AWS
Cloud platform used to deploy, scale and host backend and AI services in production.
Azure
Cloud platform used for deployment, AI services and enterprise-grade hosting.
Hands-On Labs
Production-style AI engineering lab scenarios, built using the same stack real AI teams ship with.
Design and build a RESTful API with FastAPI, backed by PostgreSQL.
Implement authentication and role-based access control with JWT.
Add background job processing using Redis-backed queues.
Containerize a backend service with Docker and automate testing with GitHub Actions.
Deploy a production-style backend service to a cloud platform.
Assessment
Knowledge Assessment
Quiz covering API design, authentication/authorization patterns, and CI/CD fundamentals.
Practical Evaluation
Students must ship a secure, tested, containerized FastAPI service deployed to the cloud with a working CI/CD pipeline.
Projects
Industry-style deliverables added directly to your project portfolio.
Backend API Service
Build and deploy a secure, production-style REST API with FastAPI, PostgreSQL/MongoDB, authentication and Docker.
What This Module Builds
Students build robust, secure and scalable backend systems with Python and FastAPI — working with relational and NoSQL databases, implementing authentication and API security, and deploying production-ready services with Docker, CI/CD and the cloud. This module lays the essential backend foundation for every AI engineering role ahead.