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5 cards from this section. Quick, good the night before a test.
What are the two main ways to deploy a trained ML model in an IoT system?
Think about where the model runs: far away on servers, or close to the sensors.
Give two benefits of edge deployment.
Think about what no longer has to travel over the network.
Which task is best suited to cloud deployment?
Look for the task that needs the most computing power and the least speed.
Why does edge deployment reduce bandwidth use?
Think about what still has to be sent once the device makes its own decisions.
A hospital wants to use wearable sensors to detect dangerous heart rhythms in patients at home. Propose a deployment plan that covers where the detection model runs, what happens if a patient's internet connection fails, how patient privacy is protected, and how the model is kept accurate over time.
Separate the life-critical, instant decision from the slower, data-hungry analysis.
An agricultural company deployed a crop-disease detection model on its field cameras last spring. By autumn, farmers report many false alarms. Explain the most likely cause and lay out a step-by-step plan to fix the problem and stop it from happening again.
Ask what changed in the images between spring and autumn, then think: monitor, retrain, validate, redeploy.
6 more questions in this section. Enroll free to practise them all.
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