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Learn how BMW builds scalable, safety-critical AI systems for automotive applications through their Connected AI Platform in this conference talk from Ray Summit 2025. Discover BMW's software-engineering-driven MLOps stack that accelerates machine learning model development and deployment across cloud and in-vehicle environments. Explore the rising demands of modern automotive AI where reliability, reproducibility, and scale are essential for real-world deployment, and understand how BMW leverages Ray, the distributed computing framework from Anyscale, to efficiently scale both classical ML workloads and large GenAI models. Examine how Ray powers distributed fine-tuning and inference of Large Language Models with strong reproducibility guarantees, robust cloud-scale training workflows for safety-critical automotive use cases, and flexible dynamic resource management across heterogeneous compute environments. Gain insights into the foundation for future multimodal workflows spanning video, text, and sensor data, plus major improvements in big-data processing that enable faster iteration and more efficient model development. Review the engineering practices that make these systems production-ready, including platform architecture decisions, operational guardrails, and lessons learned from integrating distributed AI into enterprise pipelines, while understanding how BMW's Connected AI Platform empowers rapid innovation while meeting the stringent reliability requirements of the automotive domain.
