A data science team is gathering requirements for a new predictive‐maintenance model that will be deployed on factory equipment. One requirement is to capture the model's relevant range of application. Which item best fulfills this specific requirement?
Estimate the yearly cloud compute costs required to run the batch-scoring service at peak load.
Document the acceptable boundaries for each input variable and the operating conditions under which model performance targets are guaranteed.
Provide the RACI matrix that defines who reviews, approves, and deploys code changes to the model.
List the database tables and columns that the ingestion pipeline must extract for model training and retraining.
The relevant range of application defines the operating boundaries in which a model's predictions are considered valid. By enumerating the allowable ranges for each input feature (for example, vibration amplitude between 0.2 g and 4 g, or ambient temperature between −10 °C and 50 °C) and the business scenarios in which those limits hold, the team establishes when the model can be trusted and when it is likely to be extrapolating. Hardware costs, ETL field lists, or governance workflows are important project artifacts, but they do not describe the conditions under which model assumptions remain true-they do not stop a practitioner from using the model on out-of-scope data.
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What does 'relevant range of application' mean in predictive maintenance?
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Why is it important to define input boundaries in predictive models?
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How does the operating condition impact model performance?