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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

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

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

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

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

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

FastAPI for ML Model Deployment

Prerequisites: Python & ML Concepts

Level: Intermediate

What You'll Learn

API Development
Develop RESTful APIs specifically designed for serving machine learning models using FastAPI.
Data Validation
Implement data validation for API requests and responses using Pydantic models.
Model Integration
Integrate serialized machine learning models (e.g., scikit-learn, TensorFlow, PyTorch) into FastAPI applications.
Asynchronous Programming
Utilize asynchronous operations within FastAPI to handle I/O-bound tasks and improve performance.
Application Structure
Structure FastAPI projects effectively for scalability and maintainability using routers and modules.
Testing
Write unit and integration tests for your FastAPI ML application using tools like TestClient.
Containerization
Package FastAPI applications with their dependencies using Docker for consistent deployment environments.

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