ActiveSampleSelection

Active sample selection step that intelligently selects high-value samples from model predictions for Semi-Supervised Learning (SSL) or Active Learning workflows using confidence-based, uncertainty-based, diversity-based, or hybrid strategies

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

Compute

Compute kind	sklearn

Functionality

Active sample selection script. Intelligently selects high-value samples from model predictions for SSL or Active Learning workflows.

Inputs (dependencies)

Input	Type	Required	Compatible producers
evaluation_data	processing_output	yes	XGBoostModelInference, LightGBMModelInference, PyTorchModelInference, XGBoostModelEval, LightGBMModelEval, PyTorchModelEval, BedrockBatchProcessing, BedrockProcessing, LabelRulesetExecution

Outputs

Output	Type
selected_samples	processing_output
selection_metadata	processing_output

Consumers (downstream steps)

Steps that declare this step as a compatible input source:

• PseudoLabelMerge

Framework requirements

Package	Version
pandas	>=1.3.0
numpy	>=1.21.0

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