Experiment tracking in MLOps is the process of recording and managing details of machine learning experiments, such as model parameters, datasets, code versions, and performance metrics. It helps data scientists compare different experiments and understand which changes improved or reduced model performance. This improves reproducibility, collaboration, and overall model management within a team. Experiment tracking tools also make it easier to audit and revisit previous results when needed. In your opinion, how important is experiment tracking in real-world MLOps workflows, and what challenges can arise without a proper tracking system?