Designing a cloud-native MLOps pipeline using databricks: A case study in industrial practice

We present a engineering case study on the design and evolution of a cloud-native MLOps pipeline in an industrial setting using Databricks. Motivated by the operational and lifecycle challenges of deploying machine-learning models at scale, the project adopted a platform-centric approach to automation, reproducibility, and scalable operation. The pipeline integrates versioned data management, experiment tracking and registry-based promotion, CI/CD for ML, and environment isolation to bridge experimentation and production. Security and data-privacy constraints are operationalized through workspace isolation, role-based access control, and secrets management integrated into deployment automation. Our methodological approach entailed an engineering case-study design, incorporating triangulation across development artifacts, CI/CD records, and collaborative design episodes. The paper delivers three contributions: (i) a platform-grounded reference architecture documenting the design decisions, trade-offs, and deliberate deviations from vendor guidance that shaped the final implementation; (ii) collaboration practices that align roles across DataOps, ModelOps, and DevOps showing how shared artifacts and platform constraints structure cross-role coordination; and (iii) recurring implementation patterns and practitioners lessons that are capability-oriented and transferable beyond the specific platform. The results provide practitioners with empirical evidence on how MLOps automation is realized and constrained in practice, filling a gap that vendor documentation and conceptual frameworks alone do not address.