In my opinion, the biggest benefit of metadata management in MLOps is better traceability, because it provides complete visibility into every stage of the machine learning lifecycle, including datasets, experiments, model versions, parameters, and performance outcomes. This makes it easier for teams to reproduce results, identify the root cause of issues, and understand how specific changes affect model performance over time. Strong traceability also supports other important advantages such as improved collaboration among teams, easier regulatory compliance, and faster model optimization by maintaining a well-documented and transparent workflow. As machine learning systems become more complex, effective metadata management becomes essential for building reliable, scalable, and accountable ML operations.