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DTSTART;VALUE=DATE:20221101
DTEND;VALUE=DATE:20221104
DTSTAMP:20260501T110637
CREATED:20220623T193151Z
LAST-MODIFIED:20220825T165052Z
UID:10000378-1667260800-1667519999@arize.com
SUMMARY:ODSC West
DESCRIPTION:
URL:https://arize.com/community-events/odsc-west-2/
LOCATION:Hyatt Regency San Francisco Airport\, United States
ATTACH;FMTTYPE=image/webp:https://arize.com/wp-content/uploads/2022/08/odsc.webp
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DTSTART;TZID=America/Los_Angeles:20221102T090000
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DTSTAMP:20260501T110637
CREATED:20221020T150851Z
LAST-MODIFIED:20221103T201355Z
UID:10000326-1667379600-1667381400@arize.com
SUMMARY:Pragmatic Applications Series: Improving Churn Models
DESCRIPTION:You are a machine learning engineer at a credit card company who is responsible for building a model to predict customer churn. After building your model\, you will need to monitor its performance\, drift\, as well as any data quality issues in production. Arize will show you how to monitor and troubleshoot performance\, drift and data quality issues in production. \nIn this workshop\, you’ll learn best practices for how to: \n\nSet-up performance\, drift and data quality monitoring to better understand how your model is performing. \nDiscover feature drifts corresponding to time periods of performance degradation and how to resolve them.\nCheck to see if explainability and algorithm bias are having an impact on your model decisions. 
URL:https://arize.com/community-events/pragmatic-applications-series-improving-churn-models/
LOCATION:Virtual\, United States
ATTACH;FMTTYPE=image/jpeg:https://arize.com/wp-content/uploads/2022/10/Pragmatic-Applications-Series-Churn.jpg
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