What should we keep track of?

In the previous lesson, you learned about the numerous options and possibilities involved in building ML pipelines, including data processing, algorithms, evaluation, and general parameters. In this exercise, you will apply your knowledge by choosing the options that are important to keep track of when running ML experiments.

The objective of this exercise is to help you understand the importance of carefully choosing and keeping track of key options when building and running ML pipelines. By identifying and tracking these options, you can effectively evaluate and compare different pipelines, make informed decisions about which pipelines to use, and optimize your ML models for better performance.

To successfully complete this exercise, you will need to carefully consider the different options presented and select the ones that are most relevant and important for tracking in your ML experiments.