Configure an AutoML pipeline that automates feature selection, model selection, and hyperparameter tuning while maintaining interpretability and production readiness.
## CONTEXT AutoML tools promise to democratize machine learning, but 50% of AutoML projects fail because teams treat them as black boxes — they run the pipeline, accept the best model, and deploy it without understanding why it was selected, whether it is production-viable, or how to maintain it when data…
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