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3 cards from this section. Quick, good the night before a test.
What is monitoring in the context of data science?
Think about how we track a model's performance after it's deployed.
What does maintenance involve for a data science model?
Consider what actions you would take if your model's performance changes.
What is the primary purpose of monitoring a data science model?
Think about what happens after a model is deployed.
True or False: Maintenance of a model involves only updating its software.
What else might we do to keep a model reliable?
Your organization has a model predicting customer churn. After six months, you notice inaccuracies in forecasts. Discuss the steps you would take to diagnose and correct the issue.
What do you need to know to restore accuracy?
Design a monitoring framework for a fraud detection model that identifies transactions. What key indicators would you track, and how would you respond to performance drift?
Consider what would alert you to fraudulent activities in a system.
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