Associating the configured model algorithm in AWS Clean Rooms ML

After you have configured the model algorithm, you are ready to associate the model algorithm to a collaboration. Associating a model algorithm makes the model algorithm available to all members of the collaboration.

The following image shows associating the configured model algorithm as the last step, after creating the container training image and configuring a model algorithm.

Console

Note

After the model algorithm is associated, it can't be edited. To make changes, you can delete the associated model algorithm and associate a new one.

To associate a custom ML model algorithm (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 Custom ML models.

3. On the Custom ML models page, choose the configured model algorithm that you want to associate to a collaboration and then choose Associate to collaboration.

4. In the Associate configured model algorithm window, choose the Collaboration that you want to associate to.

5. Choose Choose collaboration.

6. On the Associate model algorithm page, for Model algorithm association details, enter a Name and optional Description.

7. For Model algorithm, choose a Configured model algorithm.

8. For Trained model export privacy configurations,

   1. To export model files, select the Model files checkbox.

   2. To export output files, select the Output files checkbox.

   3. Enter a Max size value for the exported data. The value must be between 0.01 and 10.

9. (Optional) If you want to send either full error logs or shorter error summaries to members, under Trained model inference job privacy configuration,

   1. Under Full logs, select one or more Account IDs from the dropdown list.

   2. (Optional) If you want to send logs that match a filter pattern, enter a Filter pattern.

   3. (Optional) If you want to add another account and optional filter pattern, choose Add log policy.

   4. Under Error summaries, select one or more Account IDs from the dropdown list.

   5. (Optional) Select one or more Entities to redact to specify which entities will be redacted from the error log or error summaries.

      • PII – redact Personally Identifiable Information

      • Numbers – redact numbers

      • Custom – redact based on the custom redaction pattern

      1. If you chose Custom in the previous step, enter a Custom redaction pattern. This logs information that matches this pattern.

      2. (Optional) If you want to add another custom redaction pattern, choose Add another custom pattern.

10. (Optional) If you want to configure trained model metrics, under Trained model metrics configuration, select a Noise level from the dropdown list.

    You can choose None, Low, Medium and High.

11. (Optional) If you want to set the maximum artifacts size, under Artifacts configuration, enter the Max artifacts size value. The value must be between 0.01 and 10.

12. (Optional) If you want to enable Tags, choose Add new tag and then enter the Key and Value pair.

13. Choose Associate.

API

To associate a custom ML model algorithm (API)

Run the following code with your specific parameters.

You also provide a privacy policy that defines who has access to the different logs, allows customers to define regex, and how much data can be exported from the training model outputs or inference results.

Note

Configured model algorithm associations are immutable.

import boto3 
acr_ml_client= boto3.client('cleanroomsml')

acr_ml_client.create_configured_model_algorithm_association(
    name='configured_model_algorithm_association_name',
    description='purpose of the association',
    configuredModelAlgorithmArn='arn:aws:cleanrooms-ml:region:account:membership/membershipIdentifier/configured-model-algorithm/identifier',
    privacyConfiguration={
        "policies": {
            "trainedModelExports": {
                "filesToExport": ['files to export'],
                "containerLogs": [
                    {
                        "allowedAccountIds": ['member_account_id'],
                        "filterPattern": ['filter pattern'],
                        "logRedactionConfiguration": {
                            "entitiesToRedact": [
                                'ALL_PERSONALLY_IDENTIFIABLE_INFORMATION',
                                'NUMBERS',
                                'CUSTOM'
                            ],
                            "customEntityConfig": {
                                "customDataIdentifiers": [
                                    'custom_regex_1',
                                    'custom_regex_2'
                                ] 
                            } 
                        } 
                    }
                ],
                "containerMetrics": {
                    "noiseLevel": 'noise value'
                },
                "maxArtifactSize": {
                    "unit": 'unit',
                    "value": 'number'
                }
            },
            "trainedModelInferenceJobs": {
                "containerLogs": [
                    {
                        "allowedAccountIds": ['member_account_id'],
                        "filterPattern": ['filter pattern'],     
                        "logRedactionConfiguration": {
                            "entitiesToRedact": [
                                'ALL_PERSONALLY_IDENTIFIABLE_INFORMATION',
                                'NUMBERS',
                                'CUSTOM'
                            ],
                            "customEntityConfig": {
                                "customDataIdentifiers": [
                                    'custom_regex_1',
                                    'custom_regex_2'
                                ] 
                            }
                        }
                    }
                ],
                "maxOutputSize": {
                    "unit": 'unit',
                    "value": 'number'
                }
            }
        }
    },
    tags={
        'tag': 'tag'
    }
)

After the configured model algorithm is associated to the collaboration, training data providers must add a collaboration analysis rule to their table. This rule allows the configured model algorithm association to access their configured table. All contributing training data providers must run the following code:

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

acr_client.create_configured_table_association_analysis_rule(
    membershipIdentifier= 'membership_id',
    configuredTableAssociationIdentifier= 'configured_table_association_id',
    analysisRuleType= 'CUSTOM',
    analysisRulePolicy = {
        'v1': {
            'custom': {
                'allowedAdditionalAnalyses': ['arn:aws:cleanrooms-ml:region:*:membership/*/configured-model-algorithm-association/*''],
                'allowedResultReceivers': []
            }
        }
    }
)

Note

Because configured model algorithm associations are immutable, we recommend that training data providers who wants to allowlist models for use to use wild cards in allowedAdditionalAnalyses during the first few iterations of custom model configuration. This allows model providers to iterate on their code without requiring other training providers to re-associate before training their updated model code with data.