FastAPI has revolutionized how we deploy machine learning models as web services. Its automatic API documentation, built-in validation, and excellent performance make it the perfect choice for ML model deployment. In this comprehensive guide, we'll build a production-ready ML API that handles image classification, text processing, and tabular data prediction. We'll cover advanced topics like async request handling, background tasks for long-running predictions, request validation with Pydantic models, and automatic OpenAPI documentation generation. I'll also demonstrate how to implement proper error handling, logging, rate limiting, and authentication. The tutorial includes deployment strategies for AWS, Azure, and containerized environments. These techniques have enabled me to deploy APIs that serve thousands of predictions daily with sub-200ms response times.