TensorFlow Serving
TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. It enables easy deployment of new algorithms and experiments while keeping the same server architecture and APIs. It supports model versioning, can serve multiple models simultaneously, and provides out-of-the-box integration with TensorFlow models.
Developers should use TensorFlow Serving when deploying TensorFlow models in production to ensure scalability, reliability, and efficient inference. It is ideal for use cases like real-time prediction services, A/B testing of model versions, and maintaining model consistency across deployments. It simplifies the serving process by handling model updates without downtime and optimizing for performance.