A data scientist is preparing a dataset in Amazon SageMaker Data Wrangler to train a gradient-boosted tree model for customer churn prediction. One feature, 'monthly_charges', is a continuous value ranging from 15 to 350. The team wants to discretize this feature into a fixed number of buckets so that each bucket contains approximately the same number of records, reducing the impact of a highly uneven distribution across the value range. Which Data Wrangler transformation should the data scientist apply?