Running machine learning models in production shouldn't feel like navigating a minefield. In this session, we'll explore KServe—the Kubernetes-native model inference platform—and how KAgent revolutionizes the way we deploy, manage, and scale AI models in real-world environments.We'll dive into how KServe provides a standardized inference protocol that works across frameworks (TensorFlow, PyTorch, scikit-learn, XGBoost), making it easy to serve models without rewriting your deployment logic every time. You'll learn about KAgent's intelligent agent-based architecture that handles model lifecycle management, automatic canary deployments, and A/B testing with minimal configuration.Whether you're dealing with GPU-accelerated inference, autoscaling based on actual prediction load, or trying to implement blue-green deployments for your ML models, this session will show you practical patterns that work in production. We'll cover real-world challenges like cold start optimization, multi-model serving on shared infrastructure, and how to integrate KServe with your existing MLOps pipelines.

This session is perfect for ML engineers, platform teams, and anyone tired of the gap between "it works in my notebook" and "it's serving predictions at scale." Come learn how KServe and KAgent bridge the gap between data science and production infrastructure.