AWS sagemaker documentation change
Summary
Removed legacy SageMaker Python SDK v2 code examples and cleaned up documentation structure
Security assessment
Changes involve removing outdated SDK v2 code samples and redundant headers. No security vulnerabilities, fixes, or security features are mentioned. The modifications appear to be routine documentation cleanup without security implications.
Diff
diff --git a/sagemaker/latest/dg/feature-store-fraud-detection-notebook.md b/sagemaker/latest/dg/feature-store-fraud-detection-notebook.md index 67af8ac50..f8834d355 100644 --- a//sagemaker/latest/dg/feature-store-fraud-detection-notebook.md +++ b//sagemaker/latest/dg/feature-store-fraud-detection-notebook.md @@ -46,3 +45,0 @@ The role that you use to run the notebook must have the following managed polici -SageMaker Python SDK v3 - - @@ -72,25 +68,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - import boto3 - import sagemaker - from sagemaker.session import Session - - sagemaker_session = sagemaker.Session() - region = sagemaker_session.boto_region_name - boto_session = boto3.Session(region_name=region) - role = sagemaker.get_execution_role() - default_bucket = sagemaker_session.default_bucket() - prefix = 'sagemaker-featurestore' - offline_feature_store_bucket = 's3://{}/{}'.format(default_bucket, prefix) - - sagemaker_client = boto_session.client(service_name='sagemaker', region_name=region) - featurestore_runtime = boto_session.client(service_name='sagemaker-featurestore-runtime', region_name=region) - - feature_store_session = Session( - boto_session=boto_session, - sagemaker_client=sagemaker_client, - sagemaker_featurestore_runtime_client=featurestore_runtime - ) - @@ -137,3 +108,0 @@ When you set up your feature groups, you need to customize the feature names wit -SageMaker Python SDK v3 - - @@ -145,8 +113,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - from sagemaker.feature_store.feature_group import FeatureGroup - feature_group_name = "some string for a name" - feature_group = FeatureGroup(name=feature_group_name, sagemaker_session=feature_store_session) - @@ -155,3 +115,0 @@ For example, in the fraud detection example, the two feature groups are `identit -SageMaker Python SDK v3 - - @@ -168,14 +125,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - import time - from time import gmtime, strftime, sleep - from sagemaker.feature_store.feature_group import FeatureGroup - - identity_feature_group_name = 'identity-feature-group-' + strftime('%d-%H-%M-%S', gmtime()) - transaction_feature_group_name = 'transaction-feature-group-' + strftime('%d-%H-%M-%S', gmtime()) - - identity_feature_group = FeatureGroup(name=identity_feature_group_name, sagemaker_session=feature_store_session) - transaction_feature_group = FeatureGroup(name=transaction_feature_group_name, sagemaker_session=feature_store_session) - @@ -197,10 +140,0 @@ You can now load the feature definitions by passing a data frame containing the -SageMaker Python SDK v3 - - - - identity_feature_group.load_feature_definitions(data_frame=identity_data); # output is suppressed - transaction_feature_group.load_feature_definitions(data_frame=transformed_transaction_data); # output is suppressed - -SageMaker Python SDK v2 (Legacy) - - @@ -215,3 +148,0 @@ In this step, you use the `create` function to create the feature group. The fol -SageMaker Python SDK v3 - - @@ -235,20 +165,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - # create a FeatureGroup - feature_group.create( - description = "Some info about the feature group", - feature_group_name = feature_group_name, - record_identifier_name = record_identifier_name, - event_time_feature_name = event_time_feature_name, - feature_definitions = feature_definitions, - role_arn = role, - s3_uri = offline_feature_store_bucket, - enable_online_store = True, - online_store_kms_key_id = None, - offline_store_kms_key_id = None, - disable_glue_table_creation = False, - data_catalog_config = None, - tags = ["tag1","tag2"]) - @@ -277,3 +187,0 @@ When you create a feature group, it takes time to load the data, and you need to -SageMaker Python SDK v3 - - @@ -284,6 +191,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - status = feature_group.describe().get("FeatureGroupStatus") - @@ -321,3 +222,0 @@ You can retrieve information about your feature group with the `describe` functi -SageMaker Python SDK v3 - - @@ -327,6 +225,0 @@ SageMaker Python SDK v3 -SageMaker Python SDK v2 (Legacy) - - - - feature_group.describe() - @@ -375,9 +267,0 @@ The SageMaker Python SDK’s `FeatureStore` class also provides the functionalit -SageMaker Python SDK v3 - - - - print(feature_group.as_hive_ddl()) - -SageMaker Python SDK v2 (Legacy) - - @@ -431,9 +314,0 @@ You can delete a feature group with the `delete` function. -SageMaker Python SDK v3 - - - - feature_group.delete() - -SageMaker Python SDK v2 (Legacy) - - @@ -444,10 +318,0 @@ The following code example is from the fraud detection example. - -SageMaker Python SDK v3 - - - - identity_feature_group.delete() - transaction_feature_group.delete() - -SageMaker Python SDK v2 (Legacy) -