Production ML workflows
ML engineering prompts: feature engineering, training pipelines, model evaluation, MLOps workflows, deployment (BentoML/Ray), monitoring (drift, quality), and retraining triggers.
Design an ML pipeline for [task]. Include: data validation, feature engineering, training, eval (with holdout), deployment (canary), monitoring (drift), and retraining trigger.
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