Contributing training data in AWS Clean Rooms ML - AWS Clean Rooms

Contributing training data in AWS Clean Rooms ML

After the collaboration creator has created the collaboration and invited members have joined, you are ready to contribute training data to the collaboration. Any member can contribute training data.

Console
To contribute training data (console)

  1. Sign in to the AWS Management Console and open the AWS Clean Rooms console at https://console.aws.amazon.com/cleanrooms.

  2. In the left navigation pane, choose Tables.

  3. On the Tables page, choose Configure new table.

  4. For Configure new table, for Data source, choose Amazon S3, Amazon Athena, or Snowflake and complete the following steps, based on your data source:

    If you're using Then
    Amazon S3

    1. Choose a Database from the dropdown list, and then select the Table from the database.

    2. For Columns allowed in collaborations, choose either All columns or Custom list.

    3. For Configured table details, provide the Name and an optional Description for this table.

    4. If you want to report model metrics, enter the Name of the metrics and the Regex statement that will search the output logs to find the metric.

    5. If you want to enable Tags for the configured table resource, choose Add new tag and then enter the Key and Value pair.

    Amazon Athena

    1. Choose a Database from the dropdown list, and then select the Table from the database.

    2. For Columns allowed in collaborations, choose either All columns or Custom list.

    3. For Configured table details, provide the Name and an optional Description for this table.

    4. If you want to report model metrics, enter the Name of the metrics and the Regex statement that will search the output logs to find the metric.

    5. If you want to enable Tags for the configured table resource, choose Add new tag and then enter the Key and Value pair.

    Snowflake

    1. Specify the Snowflake credentials using an existing secret ARN or storing a new secret for this table.

    2. For Snowflake table and schema details, enter the details manually or automatically import the details.

    3. For the Schema, enter the Column name and choose the Data type from the dropdown list.

    4. If you want to enable Tags for the configured table resource, choose Add new tag and then enter the Key and Value pair.

  5. Choose Configure new table.

  6. On the table details page, choose Configure analysis rule to configure a custom analysis rule for this table. A custom analysis rule limits access to your data. You can either allow a specific set of pre-authorized queries on your data or allow a specific set of accounts to query your data.

    1. For Analysis rule type, choose Custom and for Creation method, choose Guided flow.

    2. Choose Next.

    3. For Specify analysis controls, choose between Review each new analysis and Allow any analysis by specific collaborators.

    4. Choose Next.

    5. (Optional) For Specify analysis results controls, for Columns not allowed in output specify whether you want to exclude any columns from the output. If you choose None, no columns are excluded from the output. If you choose Custom list, you can specify certain columns that will be removed from the output.

    6. For Additional analyses applied to output specify whether you want to allow, deny, or require an additional analysis before results are generated.

    7. Choose Next.

    8. (Optional) For Set differential privacy, choose Turn off.

    9. Choose Next.

    10. Review the information on the Review and configure page, then choose Configure analysis rule.

  7. From the table details page, choose Associate to collaboration.

  8. In the Associate table dialog box, select the collaboration that you want to associate this table to and choose Choose collaboration.

  9. On the Associate table page, review and verify the information in Table association details, Service access, and Tags. Choose Associate table.

  10. In the Tables associated by you table, select the radio button next to the table that you just associated. From the Actions menu, choose Configure in the Collaboration analysis rule group.

  11. On the Configure collaboration analysis rule page, for Allowed additional analyses, choose whether any collaboration members or specific collaboration members can perform additional analyses.

    For Results delivery, choose which members are allowed to receive results from query outputs.

  12. Choose Configure analysis rule.

API

To contribute training data (API)

  1. Configure an existing AWS Glue table for use in AWS Clean Rooms by providing the table and the columns that can be used.

    Run the following code with your specific parameters.

    import boto3 
acr_client= boto3.client('cleanrooms')

acr_client.create_configured_table(
    name='configured_table_name',
    tableReference= {
        'glue': {
            'tableName': 'glue_table_name',
            'databaseName': 'glue_database_name'
        }
    },
    analysisMethod="DIRECT_QUERY",
    allowedColumns=["column1", "column2", "column3",...]
)

  2. Configure a custom analysis rule that limits access to your data. You can either allow a specific set of pre-authorized queries on your data or allow a specific set of accounts to query your data.

    Run the following code with your specific parameters.

    import boto3 
acr_client= boto3.client('cleanrooms')

acr_client.create_configured_table_analysis_rule(
    configuredTableIdentifier='configured_table_id',
    analysisRuleType='CUSTOM',
    analysisRulePolicy= {
        'v1': {
            'custom': {
                'allowedAnalyses': ['ANY_QUERY'],
                'allowedAnalysisProviders': ['query_runner_account'],
                'additionalAnalyses': "REQUIRED"
            }
        }
    }
)

    In this example, a specific account is allowed to run any query on the data and an additional analysis is required.

  3. Associate a configured table to the collaboration and provide a service access role to the AWS Glue tables.

    Run the following code with your specific parameters.

    import boto3 
acr_client= boto3.client('cleanrooms')

acr_client.create_configured_table_association(
    name='configured_table_association_name',
    membershipIdentifier='membership_id',
    configuredTableIdentifier='configured_table_id',
    roleArn='arn:aws:iam::account:role/role_name'
)
    Note

    This service role has permissions to the tables. The service role is assumable only by AWS Clean Rooms to run allowed queries on behalf of the member who can query. No collaboration members (other than the data owner) have access to the underlying tables in the collaboration. The data owner can turn off differential privacy to make their tables available for querying by other members.

  4. Finally, add an analysis rule to the configured table association.

    Run the following code with your specific parameters.

    import boto3
acr_client= boto3.client('cleanrooms')

acr_client.create_configured_table_association_analysis_rule(
    configuredTableAssociationIdentifier='configured_table_association_identifier',
    membershipIdentifier='membership_id',
    configuredTableIdentifier='configured_table_id',
    analysisRuleType = 'CUSTOM',
    analysisRulePolicy= {
        'v1': {
            'custom': {
                'allowedAdditionalAnalyses': ['configured_model_algorithm_association_arns'],
                'allowedResultReceivers': ['query_runner_account']
            }
        }
    }
)