# Data Privacy in Amazon SageMaker AI
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Amazon SageMaker AI collects aggregate information about the use of AWS-owned and open source libraries used during training. SageMaker AI uses this aggregate metadata to improve services and customer experience.

The following sections provide explanations for the type of metadata that SageMaker AI collects and how to opt out of metadata collection.

## Types of information collected
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**Usage Information**  
Metadata from AWS-owned and open source libraries that are used with SageMaker training, such as those used for distributed training, compilation, and quantization.

**Errors**  
Errors from unexpected behavior including failures, crashes, cascades, and failures that result from interacting with the SageMaker training platform.

## How to opt out of metadata collection
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You can opt out of sharing aggregated metadata with SageMaker training when creating a training job using the `CreateTrainingJob` API. If you are using the console to create training jobs, metadata collection is disabled by default.

**Important**  
You must choose to opt out of metadata collection for each training job that you submit. You must also choose to opt out in an API call as shown in the following examples. You cannot choose to opt out inside a training script.

The following section shows how you can opt out of metadata collection using the AWS CLI, AWS SDK for Python (Boto3), or the SageMaker Python SDK.

### Opt out of metadata collection using the AWS Command Line Interface (AWS CLI)
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To opt out of metadata collection using the AWS CLI, set the environment variable `OPT_OUT_TRACKING` to `1` in the `create-training-job` API as shown in the following code example.

```
aws sagemaker create-training-job \
--training-job-name your_job_name \
--algorithm-specification AlgorithmName=your_algorithm_name\
--output-data-config S3OutputPath=s3://bucket-name/key-name-prefix \
--resource-config InstanceType=ml.c5.xlarge, InstanceCount=1 \
--stopping-condition MaxRuntimeInSeconds=100 \
--environment OPT_OUT_TRACKING=1
```

### Opt out of metadata collection using the AWS SDK for Python (Boto3)
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To opt out of metadata collection using the SDK for Python (Boto3), set the environment variable `OPT_OUT_TRACKING` to `1` in the `create_training_job` API as shown in the following code example.

```
boto3.client('sagemaker').create_training_job(
    TrainingJobName='your_training_job',
    AlgorithmSpecification={
        'AlgorithmName': 'your_algorithm_name',
        'TrainingInputMode': 'File',
    },
    RoleArn='your_arn',
    OutputDataConfig={
        'S3OutputPath': 's3://bucket-name/key-name-prefix',
    },
    ResourceConfig={
        'InstanceType': 'ml.m4.xlarge',
        'InstanceCount': 1,
        'VolumeSizeInGB': 123,
    },
    StoppingCondition={
        'MaxRuntimeInSeconds': 123,
    },
    Environment={
        'OPT_OUT_TRACKING': '1'
    },
)
```

### Opt out of metadata collection using the SageMaker Python SDK
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To opt out of metadata collection using the SageMaker Python SDK, set the environment variable `OPT_OUT_TRACKING` to `1` inside a SageMaker AI estimator as shown in the following code example.

```
sagemaker.estimator(
    image_uri='path_to_container',
    role='rolearn',
    instance_count=1, 
    instance_type='ml.c5.xlarge', 
    environment={
        'OPT_OUT_TRACKING': '1'
    }, 
)
```

### Opt out of metadata collection account-wide
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If you want to opt-out of metadata collection for several accounts, you can set an environment variable to opt-out of tracking account-wide. You must use the SageMaker AI Python SDK to opt out of metadata collection at an account level.

The following code example shows how opt out of tracking account-wide.

```
SchemaVersion: '1.0'
SageMaker:
  TrainingJob:
    Environment:
      'OPT_OUT_TRACKING': '1'
```

For more information about how to opt out of tracking account-wide, see [Configuring and using defaults with the SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/overview.html#id22).

## Additional information
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**If your downstream service depends on SageMaker AI training**

If you operate a service that relies on SageMaker training, it is highly recommended that you inform your customer about aggregate metadata collection in the SageMaker Training platform and present them with the choice to opt out. Alternatively, you can opt out of metadata collection on behalf of your customer.

**If you are a client or a customer of a service that uses SageMaker AI training**

If you are a client or customer of a service that uses SageMaker training, use your preferred method in the previous section to opt out of metadata collection.