# Associating the configured model algorithm in AWS Clean Rooms ML
<a name="associate-model-algorithm"></a>

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.

![\[An overview of how to contribute a custom ML model.\]](http://docs.aws.amazon.com/clean-rooms/latest/userguide/images/bringMLModelCollaboration.png)


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#### [ 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](https://console.aws.amazon.com/cleanrooms/home).

1. In the left navigation pane, choose **Custom ML models**.

1. 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**.

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

1. Choose **Choose collaboration**.

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

1. For **Model algorithm**, choose a **Configured model algorithm**.

1. For **Trained model export privacy configurations**,

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

   1. To export output files, select the **Output files** checkbox.

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

1. (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.

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

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

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

   1. (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.

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

1. (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**.

1. (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.

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

1. Choose **Associate**.

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#### [ 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.

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