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13. Monitoring Live data's quality

Einstein Studio's demo playlist explores the full model lifecycle, with Chapter 13 focusing on monitoring live data quality for predictive AI. This chapter highlights the importance of ensuring trustworthy data feeds for live models, discussing two critical data quality signals: out-of-bounds values and missing values. Users learn how to track these metrics directly within the platform, allowing for proactive management of data issues before they affect model performance and business decisions. The chapter emphasizes the benefits of maintaining data quality, including improved predictability, reduced investigation time, and enhanced compliance.