Course

Building AI Apps with FastAPI

Building an AI-powered application means more than writing model calls in a notebook. You need an API layer so other systems can interact with your model, containers so your application runs the same way everywhere, and orchestration so services like databases work alongside your app. This course takes you through that full arc — building an LLM-powered API with FastAPI, packaging it with Docker, connecting it to PostgreSQL with Docker Compose, and applying hardening patterns like health checks, multi-stage builds, and non-root execution.

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Part of the AI Engineering in Python path.

4.8 (359 reviews)

4 learners enrolled in this course.

Intermediate friendly
8 hours
Self paced
4 lessons

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Otávio Silveira

Data Analyst @ Hortifruti

Building AI Apps with FastAPI

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Course overview

AI applications need more than model code — they need APIs, containers, and orchestration. In this course, you’ll build an LLM-powered API with FastAPI, containerize it with Docker, connect it to a database using Docker Compose, and apply production hardening patterns. You’ll go from a working API endpoint to a fully orchestrated, deployment-ready application stack.

Key skills

Building HTTP APIs that integrate LLM calls with structured request and response handling
Containerizing AI applications with Docker and running them in isolated environments
Orchestrating multi-container stacks using Docker Compose
Connecting FastAPI services to PostgreSQL through Compose networking
Securing and hardening containers for production-ready deployments

Course outline

Building AI Apps with FastAPI [4 lessons]

Build an LLM API with FastAPI 2h

Lesson Objectives

Create FastAPI applications and define HTTP endpoints
Enforce API contracts using Pydantic request/response models
Integrate LLM calls within FastAPI endpoint functions
Handle async operations and blocking calls appropriately
Implement structured error handling with HTTP exceptions

Introduction to Docker for AI Engineering 2h

Lesson Objectives

Understand Docker containers and images as isolated environments
Write a Dockerfile to package FastAPI applications
Build Docker images and run them as containers
Pass environment variables securely into running containers
Manage containers using CLI commands and Docker Desktop

Multi-Container Applications with Docker Compose for AI Engineering 2h

Lesson Objectives

Define multi-service applications using compose.yaml configuration files
Connect FastAPI applications to PostgreSQL through Docker Compose networking
Implement named volumes for persistent database storage across restarts
Manage application stacks using docker compose CLI commands
Distinguish between Dockerfile build instructions and Compose orchestration configuration

Advanced Concepts in Docker Compose for AI Engineering 2h

Lesson Objectives

Add health checks to ensure database readiness
Externalize credentials using .env files and variable interpolation
Implement multi-stage builds to optimize Docker images
Configure containers to run as non-root users
Apply version tagging for reproducible image deployments

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