Rishi Mondal
CNCF KubeStellar Maintainer | Docker Captain | LFX Mentor | 2× GSoC | SRE at Obmondo
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Rishi Mondal is a CNCF KubeStellar Maintainer, SRE at Obmondo, and Docker Captain . A Linux Foundation Mentor, GSoC Mentor at CNCF, and OWASP Mentor he uniquely progressed from LFX Mentee to Maintainer to Mentor within a single year. A regular cloud-native speaker at multiple events across Asia . he was personally featured by the CNCF CEO at Kubecon India for community impact . Harvard HPAIR Delegate and LIFT Scholar.
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cncf kubestellar managing multicluster
Kubestellar is a tool for managing multiple Kubernetes clusters from a single interface. It allows easy switching between clusters, visualizes resources, and simplifies workflows across environments. By using unified dashboards, it offers a human-friendly way to manage multi-cluster setups with features like context switching, monitoring, and multi-cluster resource management. This simplifies the complexities of working with multiple clusters, providing a seamless experience for users.
KServe & KAgent: Simplifying AI Model Deployment at Scale
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.
Chandigarh Meetup Sessionize Event
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