Propose a framework for incorporating user feedback into the iterative improvement of ML models in production, ensuring compliance with data privacy regulations.
Instruction: Describe a system that collects, processes, and leverages user feedback for the improvement of ML models, with a strong emphasis on maintaining user privacy and data protection throughout.
Context: This question probes the candidate's ability to design a feedback loop that not only improves ML models based on user interactions but also strictly adheres to data privacy and protection standards. The candidate needs to detail a method for collecting user feedback in a way that respects privacy, a process for integrating this feedback into model training datasets, and a strategy for continuous model improvement based on this feedback, all while complying with relevant data protection regulations.
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I would design the feedback loop with three layers: feedback collection, privacy filtering and permission checks, and model-improvement workflows. Not all feedback should flow straight into training...