Scale LLM fine-tuning with Training Hub and OpenShift AI

This blog post discusses how data science teams can scale fine-tuning of large language models (LLMs) using Red Hat's Training Hub and OpenShift AI. It provides a four-step guide: starting local experiments, moving to OpenShift AI interactive notebooks, scaling with Kubeflow Trainer, and operationalizing workflows with AI pipelines and Model Registry. The goal is to transition from local experiments to production-grade workflows effectively while maintaining the same algorithmic code.