Ray Serve - Scalable ML Model Serving Platform

Ray Serve is a scalable model serving library for building online inference APIs. Built on Ray, it enables you to serve machine learning models at scale with minimal code changes. Deploy single models or compose multiple models into a single application with automatic scaling and load balancing.

• Framework agnostic - works with TensorFlow, PyTorch, Scikit-learn, XGBoost and more
• Automatic scaling based on request load with zero-downtime deployments
• Multi-model composition for complex ML pipelines and ensemble serving
• Built-in monitoring, logging, and health checks for production reliability
• HTTP and gRPC APIs with async request handling

Deploy your first model in minutes using the pre-configured Docker container. The service runs on port 8000 and provides REST APIs for model inference. Simply mount your model files and start serving predictions with enterprise-grade reliability and performance.

Disclaimer: This software is open-source and distributed under its own licensing terms. It is not affiliated with, endorsed by, or sponsored by the upstream project maintainers. Provided "as is" without warranty. Users utilize this software at their own risk and are responsible for compliance with applicable regulations.