MissingValueImputation

Missing value imputation step using statistical methods (mean, median, mode, constant) with pandas-safe values

SageMaker step type	Processing
Node type	internal (consumes upstream, produces downstream)
Container entry point	missing_value_imputation.py
Interface file	steps/interfaces/missing_value_imputation.step.yaml

Compute

Compute kind	sklearn

Functionality

Missing value imputation script. Handles missing values using statistical methods (mean, median, mode, constant). Training mode fits imputers; inference applies pre-fitted. Per-column strategies can also be supplied dynamically via COLUMN_STRATEGY_<column_name> environment variables (discovered at runtime, not pre-declared).

Inputs (dependencies)

Input	Type	Required	Compatible producers
input_data	processing_output	yes	TabularPreprocessing, StratifiedSampling, RiskTableMapping, ProcessingStep
model_artifacts_input	processing_output	no	MissingValueImputation_Training, ProcessingStep

Outputs

Output	Type
processed_data	processing_output
model_artifacts_output	processing_output

Consumers (downstream steps)

Steps that declare this step as a compatible input source:

• BedrockBatchProcessing

• BedrockProcessing

• CurrencyConversion

• FeatureSelection

• LabelRulesetExecution

• LightGBMMTTraining

• LightGBMTraining

• PseudoLabelMerge

• PyTorchTraining

• RiskTableMapping

• XGBoostTraining

• XgboostMtTraining

Framework requirements

Package	Version
pandas	>=1.3.0
numpy	>=1.21.0
scikit-learn	>=1.0.0

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