---
title: "GET /collaborations/{collaborationIdentifier}/ml-input-channels/{mlInputChannelArn}"
method: GET
path: "/collaborations/{collaborationIdentifier}/ml-input-channels/{mlInputChannelArn}"
---

# GET /collaborations/{collaborationIdentifier}/ml-input-channels/{mlInputChannelArn}

`GET /collaborations/{collaborationIdentifier}/ml-input-channels/{mlInputChannelArn}`

Returns information about a specific ML input channel in a collaboration.

## Path parameters

- `mlInputChannelArn` string, required
- `collaborationIdentifier` string, required

## Response `200`

Success

- GetCollaborationMLInputChannelResponse
  - `membershipIdentifier` string, required — The membership ID of the membership that contains the ML input channel.
  - `collaborationIdentifier` string, required — The collaboration ID of the collaboration that contains the ML input channel.
  - `mlInputChannelArn` string, required — The Amazon Resource Name (ARN) of the ML input channel.
  - `name` string, required — The name of the ML input channel.
  - `configuredModelAlgorithmAssociations` ConfiguredModelAlgorithmAssociationArn[], required — The configured model algorithm associations that were used to create the ML input channel.
  - `status` 'CREATE_PENDING' | 'CREATE_IN_PROGRESS' | 'CREATE_FAILED' | 'ACTIVE' | 'DELETE_PENDING' | 'DELETE_IN_PROGRESS' | 'DELETE_FAILED' | 'INACTIVE', required — The status of the ML input channel.
  - `statusDetails` StatusDetails — Details about the status of a resource.
    - `statusCode` string — The status code that was returned. The status code is intended for programmatic error handling. Clean Rooms ML will not change the status code for existing error conditions.
    - `message` string — The error message that was returned. The message is intended for human consumption and can change at any time. Use the <code>statusCode</code> for programmatic error handling.
  - `retentionInDays` integer, required — The number of days to retain the data for 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.
  - `creatorAccountId` string, required — The account ID of the member who created the ML input channel.

## Other responses

- `480` — ValidationException
- `481` — AccessDeniedException
- `482` — ResourceNotFoundException
- `483` — ThrottlingException

---

[API](https://skmtc.net/aws/apis/cleanroomsml.md) · [All operations](https://skmtc.net/aws/apis/cleanroomsml/llms.txt) · [OpenAPI document](https://skmtc-service-staging.skmtc.workers.dev/v1/apis/aws/cleanroomsml/versions/d3bb6d6eaa9a/schema)