statusCode for programmatic error handling.
- `retentionInDays` integer, required — The number of days to keep the data in the ML input channel.
- `numberOfRecords` integer — The number of records in the ML input channel.
- `privacyBudgets` object — Returns the privacy budgets that control access to this Clean Rooms ML input channel. Use these budgets to monitor and limit resource consumption over specified time periods.
- `accessBudgets` AccessBudget[] — A list of access budgets that apply to resources associated with this Clean Rooms ML input channel.
- `resourceArn` string, required — The Amazon Resource Name (ARN) of the resource that this access budget applies to.
- `details` AccessBudgetDetails[], required — A list of budget details for this resource. Contains active budget periods that apply to the resource.
- `startTime` string, date-time, required — The start time of this budget period.
- `endTime` string, date-time — The end time of this budget period. If not specified, the budget period continues indefinitely.
- `remainingBudget` integer, required — The amount of budget remaining in this period.
- `budget` integer, required — The total budget amount allocated for this period.
- `budgetType` 'CALENDAR_DAY' | 'CALENDAR_MONTH' | 'CALENDAR_WEEK' | 'LIFETIME', required — The type of budget period. Calendar-based types reset automatically at regular intervals, while LIFETIME budgets never reset.
- `autoRefresh` 'ENABLED' | 'DISABLED' — Specifies whether this budget automatically refreshes when the current period ends.
- `aggregateRemainingBudget` integer, required — The total remaining budget across all active budget periods for this resource.
- `description` string — The description of the ML input channel.
- `syntheticDataConfiguration` object — The synthetic data configuration for this ML input channel, including parameters for generating privacy-preserving synthetic data and evaluation scores for measuring the privacy of the generated data.
- `syntheticDataParameters` object, required — The parameters that control how synthetic data is generated, including privacy settings, column classifications, and other configuration options that affect the data synthesis process.
- `epsilon` number, double, required — The epsilon value for differential privacy, which controls the privacy-utility tradeoff in synthetic data generation. Lower values provide stronger privacy guarantees but may reduce data utility.
- `maxMembershipInferenceAttackScore` number, double, required — The maximum acceptable score for membership inference attack vulnerability. Synthetic data generation fails if the score for the resulting data exceeds this threshold.
- `columnClassification` object — Classification details for data columns that specify how each column should be treated during synthetic data generation.
- `columnMapping` SyntheticDataColumnProperties[], required — A mapping that defines the classification of data columns for synthetic data generation and specifies how each column should be handled during the privacy-preserving data synthesis process.
- `columnName` string, required — The name of the data column as it appears in the dataset.
- `columnType` 'CATEGORICAL' | 'NUMERICAL', required — The data type of the column, which determines how the synthetic data generation algorithm processes and synthesizes values for this column.
- `isPredictiveValue` boolean, required — Indicates if this column contains predictive values that should be treated as target variables in machine learning models. This affects how the synthetic data generation preserves statistical relationships.
- `syntheticDataEvaluationScores` object — Evaluation scores that assess the quality and privacy characteristics of the generated synthetic data, providing metrics on data utility and privacy preservation.
- `dataPrivacyScores` object, required — Privacy-specific evaluation scores that measure how well the synthetic data protects individual privacy, including assessments of potential privacy risks such as membership inference attacks.
- `membershipInferenceAttackScores` MembershipInferenceAttackScore[], required — Scores that evaluate the vulnerability of the synthetic data to membership inference attacks, which attempt to determine whether a specific individual was a member of the original dataset.
- `attackVersion` 'DISTANCE_TO_CLOSEST_RECORD_V1', required — The version of the membership inference attack, which consists of the attack type and its version number, used to generate this privacy score.
- `score` number, double, required — The numerical score representing the vulnerability to membership inference attacks.
- `payerConfiguration` object — The payer configuration for the ML input channel.
- `computePayerAccountId` string — The account ID of the member that is responsible for paying compute costs.
- `syntheticDataPayerAccountId` string — The account ID of the member that is responsible for paying synthetic data generation costs.
- `createTime` string, date-time, required — The time at which the ML input channel was created.
- `updateTime` string, date-time, required — The most recent time at which the ML input channel was updated.
- `inputChannel` object, required — The input channel that was used to create the ML input channel.
- `dataSource` object, required — The data source that is used to create the ML input channel.
- `protectedQueryInputParameters` ProtectedQueryInputParameters — Provides information necessary to perform the protected query.
- `sqlParameters` ProtectedQuerySQLParameters, required — The parameters for the SQL type Protected Query.
- `queryString` string — The query string to be submitted.
- `analysisTemplateArn` string — The Amazon Resource Name (ARN) associated with the analysis template within a collaboration.
- `parameters` object — The protected query SQL parameters.
- `computeConfiguration` object — Provides configuration information for the workers that will perform the protected query.
- `worker` object — The worker instances that will perform the compute work.
- `type` 'CR.1X' | 'CR.4X' — The instance type of the compute workers that are used.
- `number` integer — The number of compute workers that are used.
- `properties` unknown
- `resultFormat` 'CSV' | 'PARQUET' — The format in which the query results should be returned. If not specified, defaults to CSV.
- `roleArn` string, required — The Amazon Resource Name (ARN) of the role used to run the query specified in the dataSource field of the input channel.
Passing a role across AWS accounts is not allowed. If you pass a role that isn't in your account, you get an AccessDeniedException error.
The optional metadata that you applied to the resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.
The following basic restrictions apply to tags:
Maximum number of tags per resource - 50.
For each resource, each tag key must be unique, and each tag key can have only one value.
Maximum key length - 128 Unicode characters in UTF-8.
Maximum value length - 256 Unicode characters in UTF-8.
If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
Tag keys and values are case sensitive.
Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Clean Rooms ML considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.