FastAPI for ML Model Deployment
Chapter 1: Introduction to FastAPI and API Fundamentals
What is FastAPI?
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
Quiz for Chapter 1
Chapter 2: Data Handling and Validation with Pydantic
Introduction to Pydantic
Defining Data Models
Request Body Validation
Response Model Definition
Handling Path and Query Parameters
Data Conversion and Constraints
Structuring Complex Data Models
Hands-on Practical: Validating ML Input Data
Quiz for Chapter 2
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
Managing Model Artifacts
Dependency Injection for Model Loading
Practice: Building a Model Prediction Service
Quiz for Chapter 3
Chapter 4: Structuring and Testing FastAPI Applications
Organizing Your Project with Routers
Separating Concerns
Managing Dependencies
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
Quiz for Chapter 4
Chapter 5: Asynchronous Operations and Performance
Understanding async and await in FastAPI Routes
When to Use Async for ML Inference
Running Blocking ML Operations
Using Background Tasks
Benefits of Asynchronous Requests for ML IO
Performance Considerations for API Endpoints
Practice: Implementing Async Operations
Quiz for Chapter 5
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
Quiz for Chapter 6
Writing a Dockerfile for a FastAPI Application
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- Dockerfile reference, Docker, 2024 - The official and most detailed guide for Dockerfile syntax, commands, and arguments, providing specific information for each instruction discussed in the section.
- Deployment and production, Sebastián Ramírez, 2024 - This section of the official FastAPI documentation offers guidance on deploying FastAPI applications, including recommendations for production use with Uvicorn and containerization strategies.
- Deployment, Encode OSS, 2024 (Encode OSS) - The official Uvicorn documentation provides instructions and best practices for deploying the ASGI server, which is essential for running FastAPI applications in a production environment, including within Docker containers.
- Best practices for writing Dockerfiles, Docker, 2024 (Docker) - This official Docker guide provides recommendations for creating efficient, maintainable, and secure Docker images, covering topics such as optimizing layers, caching, and reducing image size, directly applicable to building Python-based applications.