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
Advantages for ML Deployment
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- FastAPI Documentation, Sebastián RamÃrez, 2024 - The official and comprehensive documentation for FastAPI, covering its core features, performance benefits, Pydantic integration, and developer experience.
- Pydantic Documentation, Samuel Colvin and Pydantic Contributors, 2024 - The authoritative documentation for Pydantic, detailing its use for data validation, parsing, and serialization, which is a core component of FastAPI.
- ASGI Specification, The ASGI Project, 2024 - The official specification for the Asynchronous Server Gateway Interface (ASGI), which provides the foundation for high-performance asynchronous web applications like those built with Starlette and FastAPI.
- Designing Machine Learning Systems: An Iterative Process for Production-Ready AI Applications, Chip Huyen, 2022 (O'Reilly Media) - Provides principles and best practices for building robust and scalable machine learning systems, addressing challenges like latency, data integrity, and reliability that FastAPI helps mitigate in deployment.