azure-ai-fine-tuning

You can automate an Azure AI Foundry fine-tuning pipeline by orchestrating dataset preparation, training job submission, evaluation, and deployment using the provided scripts. This enables hands-off, CI/CD-integrated end-to-end workflows with built-in guardrails.

SFT, DPO, and RFT are distinct training pipelines supported for fine-tuning workflows. The Skill handles dataset creation, validation, and format conversion specific to each pipeline type, ensuring correct data structures for supervised, preference-based, and reinforcement training.

Yes, Azure Foundry training jobs are designed for CI/CD integration. The Skill provides automation-friendly prompts and scripts to submit, monitor, and calibrate training jobs, allowing teams to iterate efficiently on model quality and deployment readiness.

Dataset preparation and validation for Azure AI fine-tuning involves format conversion and structural checks for SFT, DPO, and RFT pipelines. The Skill automates this data prep step to ensure training data meets Azure Foundry requirements before job submission.

Yes, the azure-ai-projects package is required. The Skill depends on it alongside openai, azure-identity, and requests to manage the deployment, evaluation, and cost-aware operations of fine-tuned models on Azure resources.

The best way to evaluate fine-tuned models on Azure is to use the built-in evaluation and deployment steps provided. The Skill orchestrates post-training evaluation and cost-aware management to verify model quality and deployment readiness.