Build production-ready APIs for your machine learning models using the high-performance FastAPI framework. This course guides you through creating efficient endpoints, implementing robust data validation with Pydantic, integrating various ML models, handling asynchronous operations, and preparing your applications for deployment using containerization techniques like Docker.
Prerequisites Python & ML Concepts
Level:
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.