A Case Study of Rapid AI Service Deployment - Iris Classification System

• 영문명: A Case Study of Rapid AI Service Deployment - Iris Classification System
• 발행기관: 한국인공지능학회
• 저자명: 이용희(Yonghee LEE)
• 간행물 정보: 『인공지능연구』Vol.11 No. 4, 29~34쪽, 전체 6쪽
• 주제분류: 복합학 > 과학기술학
• 파일형태: PDF
• 발행일자: 2023.12.31

영문 초록

The flow from developing a machine learning model to deploying it in a production environment suffers challenges. Efficient and reliable deployment is critical for realizing the true value of machine learning models. Bridging this gap between development and publication has become a pivotal concern in the machine learning community. FastAPI, a modern and fast web framework for building APIs with Python, has gained substantial popularity for its speed, ease of use, and asynchronous capabilities. This paper focused on leveraging FastAPI for deploying machine learning models, addressing the potentials associated with integration, scalability, and performance in a production setting. In this work, we explored the seamless integration of machine learning models into FastAPI applications, enabling real-time predictions and showing a possibility of scaling up for a more diverse range of use cases. We discussed the intricacies of integrating popular machine learning frameworks with FastAPI, ensuring smooth interactions between data processing, model inference, and API responses. This study focused on elucidating the integration of machine learning models into production environments using FastAPI, exploring its capabilities, features, and best practices. We delved into the potential of FastAPI in providing a robust and efficient solution for deploying machine learning systems, handling real-time predictions, managing input/output data, and ensuring optimal performance and reliability.

목차

1. Introduction

2. Iris Classification Problem

3. Service Deployments

4. Summary

References

키워드

Machine Learning Classification Web Service FastAPI