Best Practices for Integrating TensorFlow and PyTorch in Web Applications.

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly vital in enhancing modern web applications. TensorFlow and PyTorch are two of the most powerful frameworks used for building and deploying AI models. Integrating these frameworks into web applications enables real-time predictions, automation, and intelligent analytics. However, integrating such systems requires careful attention to model optimization, scalability, and security. This paper presents best practices for integrating TensorFlow and PyTorch in web applications, focusing on efficient deployment, asynchronous inference, and secure APIs. It explores real-world applications such as face recognition, text analysis, and medical image analysis while addressing limitations in performance, interoperability, and security. The research concludes that following structured best practices enables developers to create scalable, efficient, and intelligent AI-driven web systems.