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2 cards from this section. Quick, good the night before a test.
What is data collection in IoT?
Think about what types of data the devices collect.
Name one method of data preprocessing.
Consider how we treat messy data.
What is not a type of data collected from IoT devices?
Consider what types of data are specifically addressed in IoT.
True or False: Concept drift results in improved model accuracy over time.
Think about whether changes in the environment can make predictions less accurate.
A smart factory observes strange vibrations from machines, but the initial data was highly noisy. How could data preprocessing help in this situation?
Think about how cleaning data can impact the clarity of information.
Discuss how to monitor a deployed ML model in an IoT system and address any concept drift that might occur.
Consider the importance of real-time feedback and adjustments.
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