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
Serializing and Deserializing ML Models
Was this section helpful?
- pickle - Python object serialization, Python Documentation, 2024 - Official documentation for Python's built-in
picklemodule, detailing its functionality, usage, and security considerations. - Joblib: Running Python functions as pipeline jobs, Joblib Contributors, 2024 - Official documentation for the
jobliblibrary, explaining its capabilities, including efficient serialization for objects with large NumPy arrays. - 4.1. Model persistence, scikit-learn developers, 2024 - Official scikit-learn documentation on saving and loading trained models, with recommendations for using
joblibfor scikit-learn objects. - Save and load Keras models, Neel Kovelamudi, Francois Chollet, 2024 - Official Keras guide on model saving and serialization, covering the SavedModel format and HDF5 files for TensorFlow/Keras models.
- Saving and Loading Models, PyTorch Documentation, 2024 - Official PyTorch documentation on saving and loading models, including saving the entire model or just the state dictionary.
© 2026 ApX Machine LearningEngineered with