The challenge is no longer how big, diverse, or distributed your data is. It’s that you can’t trust it. Companies are utilizing rules and metrics to monitor data quality, but they’re tedious to set up and maintain. We will present a set of fully unsupervised machine learning algorithms for monitoring data quality at scale, which requires no setup, catching unexpected issues and preventing alert fatigue by minimizing false positives. At the end of this talk, participants will be equipped with insight into unsupervised data quality monitoring, its advantages and limitations, and how it can help scale trust in your data.
Talk by: Vicky Andonova
Here’s more to explore:
LLM Compact Guide: https://dbricks.co/43WuQyb
Big Book of MLOps: https://dbricks.co/3r0Pqiz
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