Develop a comprehensive approach for managing and mitigating the impact of outliers in training data on model performance in an MLOps context.
Instruction: Outline an end-to-end process that includes the detection, analysis, and treatment of outliers in training data, and how these steps integrate into the continuous lifecycle management of ML models.
Context: This question evaluates the candidate's understanding of the impact that outliers can have on the training and performance of ML models, as well as their ability to implement a robust system for managing outliers as part of an MLOps pipeline. The candidate should discuss methods for automatically detecting outliers in data streams, techniques for assessing whether outliers should be removed or used for training, and strategies for continuously monitoring and adapting to new outliers in a production environment.
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I would start by separating true rare-but-important cases from bad data, because treating all outliers the same is a mistake...