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
Performance Considerations for API Endpoints
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- Concurrency and async/await, Sebastián Ramírez, 2024 - Details how FastAPI leverages asynchronous programming, the event loop, and
run_in_threadpoolto manage concurrent I/O-bound and CPU-bound tasks effectively. - Designing Machine Learning Systems: New Ways of Thinking About Data, Programs, Experiments, and Products, Chip Huyen, 2022 (O'Reilly Media) - Explores architectural patterns and engineering practices for building production-grade ML systems, with sections relevant to model serving performance, scalability, and resource management.
- Fluent Python, 2nd Edition, Luciano Ramalho, 2022 (O'Reilly Media) - Provides an in-depth explanation of Python's concurrency model, including
asyncio, coroutines, and the event loop, crucial for optimizing asynchronous applications like FastAPI. - Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Martin Kleppmann, 2017 (O'Reilly Media) - Offers a deep understanding of the fundamental principles behind building robust and scalable data systems, including detailed discussions on latency, throughput, serialization, and distributed system concerns critical for high-performance APIs.
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