FeatureGroup

sagemaker.services.k8s.aws/v1alpha1

Metadata#

Amazon SageMaker Feature Store stores features in a collection called Feature Group. A Feature Group can be visualized as a table which has rows, with a unique identifier for each row where each column in the table is a feature. In principle, a Feature Group is composed of features and values per features.

Spec#

description: string
eventTimeFeatureName: string
featureDefinitions:
  collectionConfig: 
    vectorConfig: 
      dimension: integer
  collectionType: string
  featureName: string
  featureType: string
featureGroupName: string
offlineStoreConfig: 
  dataCatalogConfig: 
    catalog: string
    database: string
    tableName: string
  disableGlueTableCreation: boolean
  s3StorageConfig: 
    kmsKeyID: string
    resolvedOutputS3URI: string
    s3URI: string
onlineStoreConfig: 
  enableOnlineStore: boolean
  securityConfig: 
    kmsKeyID: string
  storageType: string
  ttlDuration: 
    unit: string
    value: integer
recordIdentifierFeatureName: string
roleARN: string
tags:
- key: string
  value: string
throughputConfig: 
  provisionedReadCapacityUnits: integer
  provisionedWriteCapacityUnits: integer
  throughputMode: string

You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AddTags.html).

For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html). For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy (https://d1.awsstatic.com/whitepapers/aws-tagging-best-practices.pdf). || tags.[].key
Optional | string
| | tags.[].value
Optional | string
| | throughputConfig
Optional | object
Used to set feature group throughput configuration. There are two modes:
ON_DEMAND and PROVISIONED. With on-demand mode, you are charged for data
reads and writes that your application performs on your feature group. You
do not need to specify read and write throughput because Feature Store accommodates
your workloads as they ramp up and down. You can switch a feature group to
on-demand only once in a 24 hour period. With provisioned throughput mode,
you specify the read and write capacity per second that you expect your application
to require, and you are billed based on those limits. Exceeding provisioned
throughput will result in your requests being throttled.


Note: PROVISIONED throughput mode is supported only for feature groups that
are offline-only, or use the Standard (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OnlineStoreConfig.html#sagemaker-Type-OnlineStoreConfig-StorageType)
tier online store. | | throughputConfig.provisionedReadCapacityUnits
Optional | integer
| | throughputConfig.provisionedWriteCapacityUnits
Optional | integer
| | throughputConfig.throughputMode
Optional | string
|

Status#

ackResourceMetadata: 
  arn: string
  ownerAccountID: string
  region: string
conditions:
- lastTransitionTime: string
  message: string
  reason: string
  status: string
  type: string
failureReason: string
featureGroupStatus: string