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
Loading Models into FastAPI Applications
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- FastAPI Documentation, Sebastián RamÃrez, 2024 - Official documentation covering FastAPI's features, including application lifecycle events (lifespan) and exception handling, relevant for managing resources and errors during model deployment.
- functools - Higher-order functions and operations on callable objects, Python Software Foundation, 2024 - Standard library documentation explaining functools.lru_cache, a decorator used for memoization and caching function call results, which is essential for efficient lazy loading of models.
- Designing Machine Learning Systems: An Iterative Approach to Development, Deployment, and Maintenance, Chip Huyen, 2022 (O'Reilly Media) - A comprehensive book on designing and deploying machine learning systems, offering insights into architectural choices, serving strategies, resource management, and reliability aspects relevant to model loading.
- Joblib: run Python functions as pipeline jobs, The Joblib Developers, 2024 - Official documentation for joblib, a Python library frequently used for persisting and loading large Python objects, including machine learning models, for efficient data management.