What strategies would you employ to ensure the continuous improvement of ML models in production?
Instruction: Outline the approaches you would take to monitor, evaluate, and iteratively improve the performance of machine learning models after deployment.
Context: This question tests the candidate's ability to apply best practices in MLOps for maintaining and enhancing the performance of ML models once they are deployed. An effective response would include strategies such as setting up robust monitoring for model performance metrics, employing A/B testing to evaluate model updates, automating the retraining process with new data, and implementing feedback loops to incorporate real-world insights into model refinement.
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I would use a loop of monitoring, diagnosis, retraining or recalibration, controlled experimentation, and post-release review...
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