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* feat: Add Feature Store Support to V3 * Add feature store tests --------- Co-authored-by: adishaa <adishaa@amazon.com>
- Add feature_store_functions_report.md documenting all 63 functions across feature_store module (excluding feature_processor) - Add comprehensive unit tests for get_feature_group_as_dataframe: * Session handling (provided, from region, from role) * Error cases (missing session/region, missing event_time) * Latest ingestion logic with event time * Query string manipulation and table placeholder * Verbose and silent logging modes * Kwargs passing to as_dataframe - Add comprehensive unit tests for prepare_fg_from_dataframe_or_file: * DataFrame and file path input handling * Session/region/role configuration * Record ID creation and validation * Event ID creation with timestamp * Duplicate record detection * Column name formatting * CSV kwargs passing * Feature definition loading
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(this PR is a continuation of PR 5490
Port
feature_processormodule to SageMaker Python SDK v3Migrates the
feature_store.feature_processormodule fromsagemaker-python-sdkv2 to the v3 modular package structure (sagemaker-mlops), along with supporting changes insagemaker-core.Changes
Import path migration — All internal imports updated from
sagemaker.feature_store.feature_processor→sagemaker.mlops.feature_store.feature_processor, and external SDK dependencies remapped to their v3 locations:sagemaker.Session→sagemaker.core.helper.session_helper.Sessionsagemaker.lineage→sagemaker.core.lineagesagemaker.remote_function→sagemaker.core.remote_functionsagemaker.workflow→sagemaker.mlops.workflowsagemaker.s3/sagemaker.utils/sagemaker.vpc_utils→sagemaker.core.*Estimator → ModelTrainer migration (
feature_scheduler.py) — Replaced the v2Estimatordict-based construction with the v3ModelTrainerAPI:Compute,Networking,StoppingCondition,SourceCode,OutputDataConfig, andTagconfig objectsPipelineSessionfor pipeline-aware executionTrainingStepnow usesstep_argsfromModelTrainer.train()instead ofestimator+inputsInput channel format change (
_config_uploader.py) —prepare_step_inputnow returnsList[Channel](usingChannel/DataSource/S3DataSourceshapes) instead ofDict[str, TrainingInput].Session helper additions (
sagemaker-core) — Added Feature Store methods toSession:delete_feature_group,describe_feature_group,create_feature_group,update_feature_group, and related config schema imports.By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.