This documentation is a draft for private preview for regions in the AWS European Sovereign Cloud. Documentation content will continue to evolve. Published: January 1, 2026.Submit a model distillation job in
Amazon Bedrock
You can perform model distillation through the Amazon Bedrock console or by sending a CreateModelCustomizationJob
request with an Amazon Bedrock control plane endpoint.
Prerequisites
When your Distillation job completes, you can analyze the results of the customization process. For more information see Analyze the results of a model customization job.
For information about setting up inference for your model, see Set up inference for a custom model.
Submit your job
- Console
-
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Sign in to the AWS Management Console with an IAM identity that has permissions to use the Amazon Bedrock console. Then, open the Amazon Bedrock console at
https://eusc-de-east-1.console.amazonaws-eusc.eu/bedrock.
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From the left navigation pane, choose Custom models under Tune.
Choose Create distillation job.
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For Distilled model details, do the
following:
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For Distilled model name, enter a
name for your distilled model.
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(Optional) For Model encryption,
select the checkbox if you want to provide a KMS key
for encrypting your job and its related artifacts.
For more information, see Encryption of custom models.
-
(Optional) Apply Tags to your
distilled model.
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For Job configuration, do the
following:
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For Job name, enter a name for
your distillation job.
-
(Optional) For Model encryption,
select the checkbox if you want to provide a KMS key
for encrypting your job and its related artifacts.
For more information, see Encryption of custom models.
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(Optional) Apply Tags to your
job.
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For Teacher model – Student model details, choose the teacher and
student models for creating your distilled model.
For more information, see Choose teacher and student models for distillation.
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For Synthetic data generation, do the
following:
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For Max response length, specify the maximum length of the synthetic responses
generated by the teacher model.
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For Distillation input dataset,
choose one of the following options:
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Directly upload to S3 location –
Specify the S3 location where you're storing the input dataset
(prompts) that'll be used for distillation. For more information,
see Option 1: Provide your own prompts for data preparation.
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Provide access to invocation logs –
Specify the S3 location where you're storing the invocation logs
with the input dataset (prompts) that'll be used for distillation.
For more information, see Option 2: Use invocation logs for data preparation.
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(Optional) For Request Metadata
Filters, specify filters if you want Amazon Bedrock to only
use certain prompts in your logs for distillation.
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Choose Read prompts or Read
prompt-response pairs depending on what you want
Amazon Bedrock to access from your logs. Keep in mind that responses
are read only if your teacher model matches the model in
your logs.
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For Distillation output, specify the S3 location where you want
to upload the metrics and reports about your distillation
job.
For more information,
see Analyze the results of a model customization job.
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For VPC settings, choose a VPC configuration for accessing the S3
bucket with your training data.
For more information, see (Optional) Protect your model customization jobs
using a VPC.
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For Service access, specify the IAM
role for accessing the S3 bucket with your training data.
Unless you use a Cross Region inference profile or VPC configurations,
you can create the role in the Amazon Bedrock console with the correct permissions automatically configured. Or you can use an existing service role.
For a job that has Amazon VPC configurations or uses a Cross Region inference profile, you must create a new service role in IAM that has the required permissions.
For more information, see Create an IAM service role for model customization.
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Choose Create distillation job to start
the distillation job. After you customize a model, you can set up inference for the model. For more information, see Set up inference for a custom model.
- API
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At minimum, you must provide the following fields to submit your model distillation job
when using the Amazon Bedrock API.
| Field |
Description |
| baseModelIdentifier |
The model identifier of the student model |
| customModelName |
The name of the new distilled model |
| jobName |
The name of the model distillation job |
| roleArn |
Role that gives Amazon Bedrock permissions to read training and validation
files and write to the output path |
| trainingDataConfig |
The Amazon S3 path that has your training data |
| outputDataConfig |
The Amazon S3 path that contains your training and validation metrics |
| distillationConfig |
Inputs required for distillation job |
| customModelKmsKeyId |
To encrypt the custom model |
| clientRequestToken |
Token to prevent the request from completing more than once |
The following fields are optional:
| Field |
Description |
| customizationType |
Set to DISTILLATION by default for distillation
jobs |
| validationDataConfig |
List of validation data Amazon S3 paths |
| jobTags |
To associate tags with the job |
| customModelTags |
To associate tags with the resulting custom model |
| vpcConfig |
VPC to protect your training data and distillation job |
To prevent the request from completing more than once, include a
clientRequestToken.
You can include the following optional fields for extra configurations.
The following is an example snippet from CreateModelCustomizationJob API. This example uses the
prompt-response pairs in the invocation log as the input data source and specifies the
filter for selecting prompt-response pairs.
"trainingDataConfig": {
"invocationLogsConfig": {
"usePromptResponse": true,
"invocationLogSource": {
"s3Uri": "string"
},
"requestMetadataFilters": {
"equals": {
"priority": "High"
}
}
}
}
Response
The response returns a jobArn of the model distillation job.
Next steps