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Exploring the Evolution of Training Data Transparency
Delving into the shift from small curated data sets to large-scale ones, this chapter explores the challenges of understanding larger data sets and the role of automated tools in describing data distributions. It also discusses the nuances of training specific types of models, emphasizing the importance of documenting findings for future reference and improving model building practices. Additionally, it highlights the use of tools like Paloma to assess model performance across diverse domains and touches on the ethical considerations in model development.