A Scalable ML Framework with Monadic Design

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Transforms the DataPod (e. g. , scaling, encoding, feature engineering) Stores any trained variables inside itself (e. g. , mean, std for scalers; trained models) This separation of data (in DataPod) and behavior (in Transformer) allows clean chaining of transformations, while also making the pipeline reproducible and deployable. Initialize a fresh DataPod with the production dataset. Apply the saved transformers (the pipeline footprint) to the new data, using their stored trained parameters. pipeline = dp.

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