FastAPI for ML Model Deployment
Chapter 1: Introduction to FastAPI and API Fundamentals
Advantages for ML Deployment
Understanding APIs and HTTP Methods
Asynchronous Programming Concepts in Python
Setting Up Your Development Environment
Your First FastAPI Application
Anatomy of a FastAPI Request/Response Cycle
Practice: Creating Simple Endpoints
Chapter 2: Data Handling and Validation with Pydantic
Response Model Definition
Handling Path and Query Parameters
Data Conversion and Constraints
Structuring Complex Data Models
Hands-on Practical: Validating ML Input Data
Chapter 3: Integrating Machine Learning Models
Serializing and Deserializing ML Models
Loading Models into FastAPI Applications
Creating Prediction Endpoints
Handling Different Input Formats
Returning Predictions and Probabilities
Dependency Injection for Model Loading
Practice: Building a Model Prediction Service
Chapter 4: Structuring and Testing FastAPI Applications
Organizing Your Project with Routers
Introduction to API Testing
Using TestClient for Unit Tests
Testing Prediction Endpoints
Logging in FastAPI Applications
Handling Configuration and Secrets
Hands-on Practical: Refactoring and Testing the Prediction Service
Chapter 5: Asynchronous Operations and Performance
Understanding async and await in FastAPI Routes
When to Use Async for ML Inference
Running Blocking ML Operations
Benefits of Asynchronous Requests for ML IO
Performance Considerations for API Endpoints
Practice: Implementing Async Operations
Chapter 6: Containerization and Deployment Preparation
Introduction to Docker for Application Packaging
Writing a Dockerfile for a FastAPI Application
Including ML Models in Docker Images
Building and Running Docker Containers
Managing Python Dependencies within Docker
Configuring Applications with Environment Variables
Preparing for Production Deployment (Gunicorn/Uvicorn)
Hands-on Practical: Containerizing the ML API
