# How do you deploy a trained neural network model?

Source: https://zilliz.com/ai-faq/how-do-you-deploy-a-trained-neural-network-model
Author: zilliz

Deploying a trained neural network model involves converting it into a format suitable for production environments and integrating it with an application or system. Frameworks like TensorFlow, PyTorch, or ONNX simplify model serialization and compatibility. 

The deployment platform determines the process: for web applications, models can be hosted on cloud platforms (e.g., AWS, GCP) and accessed via APIs; for mobile or embedded devices, models are optimized using libraries like TensorFlow Lite or PyTorch Mobile. Optimization techniques like quantization or pruning reduce model size and improve inference speed without significantly impacting accuracy. 

Continuous monitoring is essential post-deployment. Tools like Prometheus or Grafana can track model performance and user feedback, ensuring the deployed model remains effective and up-to-date with changing requirements.