Learn How to Reliably Monitor Your Data and Model Quality in the Lakehouse

Developing and upkeep of production data engineering and machine learning pipelines is a challenging process for many data teams. Even more challenging is monitoring the quality of your data and models once they go into production. Building upon untrustworthy data can cause many complications for data teams. Without a monitoring service, it is challenging to proactively discover when your ML models degrade over time, and the root causes behind it. Furthermore, with a lack of lineage tracking, it is even more painful to debug errors in your models and data. Databricks Lakehouse Monitoring offers a unified service to monitor the quality of all your data and ML assets.
In this session, you’ll learn how to:
- Use one unified tool to monitor the quality of any data product: data or AI
- Quickly diagnose errors in your data products with root cause analysis
- Set up a monitor with low friction, requiring only a button click or a single API call to start and automatically generate out-of-the-box metrics
- Enable self-serve experiences for data analysts by providing reliability status for every data asset
Talk by: Kasey Uhlenhuth and Alkis Polyzotis
Connect with us: Website: databricks.com
Twitter: / databricks
LinkedIn: / databricks
Instagram: / databricksinc
Facebook: / databricksinc

[@tingxie2292]

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[@tingxie2292]

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[@tingxie2292]

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[@tingxie2292]

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[@majidafra]

When I choose Timeseries as the profile type and set all the required fields it seems to be working fine, but when I open the dashboard it throws an error like "Table or View {catalog}.{schema}.my_table_profile_metrics Can not be found". should I create the my_table_profile_metrics myself or it is a part of the process?

[@user-ze4nh9ul2y]

In my Databricks Catalog tables, that DATA QUALITY tab is missing.

[@yatharthm22]

In my case the two metric tables are not getting created automatically