FastAPI has become a popular choice for serving machine learning models due to its simplicity and performance. However, the process involves more than just loading a model and wrapping it in a route.
When serving models in real-world applications, developers face challenges such as handling concurrent requests, managing resources, and ensuring low latency. Understanding these factors is crucial for optimal performance.
This article delves into the intricacies of using FastAPI for machine learning endpoints, providing insights that can help developers navigate the complexities of serving AI models effectively.