KServe

**KServe** is an open-source framework for **serving machine learning models on Kubernetes**, enabling scalable and production-ready deployment of models for inference. It supports multiple ML frameworks and automates tasks like scaling, versioning, and inference routing, allowing developers and organizations to deploy models as REST or gRPC endpoints in cloud-native environments.

Features of KServe:

• Supports serving models from multiple frameworks including TensorFlow, PyTorch, XGBoost, ONNX, and more.
• Enables production-ready model deployment with automatic scaling, logging, and monitoring.
• Works with Kubernetes, leveraging cloud-native features for reliability and scalability.
• Includes pre-built sample deployment manifests for testing and rapid setup.
• Modular, extensible, and widely used in enterprise ML workflows and research.

To check the installed version of KServe in your environment:

 $ sudo su
 $ sudo apt update 
 $ source /opt/kserve/venv/bin/activate
 $ pip show kserve 

Disclaimer: KServe is designed for serving machine learning models in Kubernetes. The deployment and performance depend on proper configuration and Kubernetes resources. Always refer to the official repository or documentation for the most accurate and up-to-date information.