ml-engineer-pro video for you have established an ML pipeline featuring various input parameters, and your objective is to explore the trade-offs among
You have established an ML pipeline featuring various input parameters, and your objective is to explore the trade-offs among different combinations of these parameters. The parameters in question include: • The input dataset • The maximum tree depth for the boosted tree regressor • The learning rate for the optimizer You need to assess the pipeline's performance for the various parameter combinations, evaluating them in terms of F1 score, training time, and model complexity. It is essential for your methodology to be reproducible, and you aim to track all runs of the pipeline on a consistent platform. What steps should you take to achieve this?