personalize_create_dataset_import_job: Creates a job that imports training data from your data...

In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

Creates a job that imports training data from your data source (an Amazon S3 bucket) to an Amazon Personalize dataset

Description

Creates a job that imports training data from your data source (an Amazon S3 bucket) to an Amazon Personalize dataset. To allow Amazon Personalize to import the training data, you must specify an IAM service role that has permission to read from the data source, as Amazon Personalize makes a copy of your data and processes it internally. For information on granting access to your Amazon S3 bucket, see Giving Amazon Personalize Access to Amazon S3 Resources.

See https://www.paws-r-sdk.com/docs/personalize_create_dataset_import_job/ for full documentation.

Usage

personalize_create_dataset_import_job(
  jobName,
  datasetArn,
  dataSource,
  roleArn,
  tags = NULL,
  importMode = NULL,
  publishAttributionMetricsToS3 = NULL
)

Arguments

jobName	[required] The name for the dataset import job.
datasetArn	[required] The ARN of the dataset that receives the imported data.
dataSource	[required] The Amazon S3 bucket that contains the training data to import.
roleArn	[required] The ARN of the IAM role that has permissions to read from the Amazon S3 data source.
tags	A list of tags to apply to the dataset import job.
importMode	Specify how to add the new records to an existing dataset. The default import mode is FULL. If you haven't imported bulk records into the dataset previously, you can only specify FULL. Specify FULL to overwrite all existing bulk data in your dataset. Data you imported individually is not replaced. Specify INCREMENTAL to append the new records to the existing data in your dataset. Amazon Personalize replaces any record with the same ID with the new one.
publishAttributionMetricsToS3	If you created a metric attribution, specify whether to publish metrics for this import job to Amazon S3

paws.machine.learning documentation built on Sept. 12, 2023, 1:14 a.m.