Tell me about a time when you encountered a conflict during the deployment of an ML model in production, and how you resolved differing opinions among team members to ensure successful deployment.

Use STAR framework: 1) Situation (context of the conflict), 2) Task (your role and goal), 3) Action (steps taken to resolve the conflict), 4) Result (outcome and impact). Focus on collaboration, data-driven decisions, and measurable outcomes. Keep language concise and action-oriented.

During a deployment of a computer vision model to production, our DevOps team insisted on using a containerized solution for scalability, while the Data Science team preferred a lightweight API for faster iteration. I organized a cross-functional meeting to align priorities, presented latency benchmarks from our staging environment, and proposed a hybrid approach using Docker with optimized inference code. By facilitating a joint code review and demonstrating how the hybrid solution met both teams' requirements, we reached consensus. The model deployed successfully with 35% lower latency than the original API design and 20% faster training cycles. This approach reduced deployment delays by 40% and improved team collaboration for future projects.