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Learn how to unify and simplify the entire lifecycle of GenAI models using KubeRay for orchestration, from training and fine-tuning to containerization, inference, and large-scale benchmarking. Discover how Devansh Ghatak from Simplismart demonstrates eliminating the complex mix of scripts, ad-hoc orchestration, and manual setup that traditionally slows ML teams down by leveraging KubeRay as a single scalable orchestration layer for running distributed model workflows on Kubernetes. Explore real-world examples showing how Ray and KubeRay streamline grid search and hyperparameter exploration, automated LoRA fine-tuning pipelines, model containerization and build workflows, and large-scale parallelized benchmarking runs. Understand how to build environment-agnostic, reproducible workflows that run seamlessly across any Kubernetes cluster, bringing consistency, speed, and reliability to the entire GenAI experimentation and deployment process while accelerating distributed ML workflows, reducing operational overhead, and providing a unified platform for managing the complete model lifecycle in modern GenAI stacks.
