Scaling MLOps with Kubeflow Pipelines and Ray for LLM Fine-tuning
The advent of Large Language Models (LLMs) has ushered in a new era of artificial intelligence, revolutionizing how businesses interact with data and customers. However, leveraging the full potential of these powerful models often requires fine-tuning them on specific datasets to align with unique business needs. This process, while immensely beneficial, presents significant challenges in terms of computational resources, data management, and operational complexity. This article explores how Kubeflow Pipelines and Ray, in combination, provide a robust and scalable MLOps framework for efficient LLM fine-tuning.