In the rapidly evolving field of AI, particularly Gen-AI, establishing best practices for Large Language Models (LLMs) that are applicable across various use-cases is crucial. As the number of new tools and frameworks focusing on different parts of the lifecycle increases, their cohesive integration becomes essential for any production-ready setup. This session explores cloud-native tools and frameworks that enable a smooth transition from proof-of-concept to production. We’ll showcase how to efficiently fine-tune LLMs with accelerators and deploy optimized models using scalable and observable tools. We’ll also discuss the lifecycle of an LLM in a Kubernetes-powered multi-node environment, from data preprocessing to model deployment and AI observability. The session aims to provide a comprehensive view of the current landscape, emphasizing the importance of a seamless LLM lifecycle and how it can be implemented in your ML infrastructure.