How would you develop and deploy a machine learning model to predict and prevent machine failure in a manufacturing setting?
Instruction: Outline the end-to-end process, from data collection and feature engineering to model development, evaluation, and deployment, including how you would monitor and update the model. Fictional case: predict a failure in the next seven days using data available at midnight. Asset A fails on day 5; B is observed failure-free through day 10; C leaves the dataset on day 3 without a failure. A preventive replacement on day 2 can remove an asset from risk. Define labels, exclusions or censoring, and an evaluation split.
Updated
Official answer available
Read the opening below, then unlock the full answer and practical guidance.
I would start with the operational question, not the model: how much lead time the plant needs, what type of failure matters most, and what action the team can take when risk is high...