Follow this 4-step process using Training Hub and OpenShift AI to transition LLM fine-tuning from local experiments to repeatable, production-grade workflows.

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A practical 4-step guide for scaling LLM fine-tuning from local experiments to production using Red Hat's Training Hub library and OpenShift AI. Step 1 covers local experimentation with Training Hub's SFT, OSFT, and LoRA APIs. Step 2 moves notebooks into OpenShift AI workbenches for cluster-backed GPU access. Step 3 scales to distributed training with Kubeflow Trainer for multi-node, multi-GPU workloads with fault tolerance and Kueue-based scheduling. Step 4 operationalizes workflows using AI pipelines (Kubeflow Pipelines) and a Model Registry for automated retraining, quality gates, lineage tracking, and controlled model promotion. The key advantage is that core Training Hub Python code remains largely unchanged across all stages while the execution environment scales underneath it.

Scale LLM fine-tuning with Training Hub and OpenShift AI

Step 1: Local experiments with Training Hub

Step 2: Bring your notebook to OpenShift AI interactive notebooks

Step 3: Scale with training jobs using Kubeflow Trainer

Step 4: Operationalize with pipelines and Model Registry