AWS SAGEMAKER AUTOML PREDICT

Runs batch inference against AWS SageMaker AutoML-trained models by deploying endpoints (or reusing active ones), scoring the prediction dataset, and optionally tearing down endpoints and artifacts to control costs. Use this worker downstream of aws_sagemaker_automl_learn whenever you need to generate predictions from previously trained SageMaker AutoML models.

When to use

Tagged: automl, aws, batch, cloud, endpoint, inference, ml, predict.

Inputs

Label	ID	Type	Default	Required	Description
Prediction Dataset	prediction_dataset	dataset	—	✓	Tabular dataset to score; must have the same column structure (features) as the training dataset used in aws_sagemaker_automl_learn — the target column may be omitted.
Models	models	dataset	—		Models metadata dataset produced by the aws_sagemaker_automl_learn worker, containing columns model_name, target_feature, input_features, and job_name; required when no active_endpoints dataset is supplied.
Active Endpoints	active_endpoints	dataset	—		Previously created and still-live SageMaker endpoint metadata dataset (same schema as the active_endpoints output of this worker); supply instead of models to skip endpoint creation and reuse running endpoints.
Cleanup Endpoints	cleanup_endpoints	boolean	True		Boolean flag (default: true) — when true, deletes the SageMaker endpoints after prediction completes to avoid ongoing inference costs; set to false only when you intend to reuse the endpoints via the active_endpoints output.
Cleanup Models and Files	cleanup_models_and_files	boolean	False		Boolean flag (default: false) — when true, deletes the SageMaker model artifacts and associated S3 files after prediction; enable only if the model will not be needed for future inference runs.

Outputs

Label	ID	Type	Description
Predictions	predictions	dataset	Tabular dataset of model predictions, one row per input record, with columns identifying the target feature and its predicted value for each model.
Model Prediction Information	model_prediction_information	dataset	Metadata dataset summarising each prediction run, including model name, job name, endpoint name, endpoint config name, input/target features, cleanup status, and any per-model errors.
Active Endpoints	active_endpoints	dataset	Dataset describing the SageMaker endpoints that remain live after execution; only populated when cleanup_endpoints is false — pass this to a subsequent invocation of this worker to skip endpoint creation.
Logs	logs	dataset	Execution log dataset with columns log_type, log_message, and log_time (Unix timestamp), capturing info and error events emitted during the prediction workflow.

Disciplines

• ai_ml.supervised.classification
• ai_ml.supervised.regression
• platform.integration

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Auto-generated from platform schema. Worker id: aws_sagemaker_automl_predict. Schema hash: e6ed288462c3. Hand-curated docs in workerexamples/ override this page when present.