How BMW builds toward Industry AI on Amazon SageMaker AI

This article describes how BMW Group builds Industry AI capabilities on Amazon SageMaker by fine-tuning task-specific LoRA adapters on engineering process data and serving multiple domain capabilities from a single endpoint.

• Fine-tuned 8B parameter model matched quality of 31B prompted baseline while achieving 25% higher compliance scores on ticket quality tasks
• Automated evaluation framework combines compliance gates and LLM judges to measure generative and discriminative task performance
• Data curation pipeline uses generator and judge models on Amazon Bedrock to produce high-quality training examples that pass domain-specific constraints
• Multi-adapter inference serves ticket quality and incident matching tasks from one shared base model using vLLM and efficient LoRA serving
• Fine-tuned adapter outperformed larger prompted model on incident matching, especially at recognizing when no match exists (70% vs 47% no-match F1)
• Reusable framework: map use cases, define evaluation baselines, curate training data aggressively, train task-specific adapters, and compose related tasks

The engagement demonstrates that task-specific fine-tuning on curated domain examples resolves failures of larger prompted models, providing a pragmatic path to Industry AI before investing in full pre-training.