# Creating assets for model customization in the UI
<a name="model-customize-open-weight-create-assets-ui"></a>

You can create and manage the dataset and evaluator assets that you can use for model customization in the UI.

## Assets
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Select **Assets** in the left hand panel and the Amazon SageMaker Studio UI and then select **Datasets**.

![\[An image containing the access to model customization.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/screenshot-open-model-16.png)


Choose **Upload Dataset** to add the dataset that you will use in your model customization jobs. By choosing the **Required data input format**, you can access a reference of dataset format to use.

![\[An image containing the access to model customization.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/screenshot-open-model-15.png)


## Evaluators
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You can also add **Reward Functions** and **Reward Prompts** for your Reinforcement Learning customization jobs.

![\[An image containing the access to model customization.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/screenshot-open-model-14.png)


The UI also provides guidance on the format required for the reward function or reward prompt.

![\[An image containing the access to model customization.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/screenshot-open-model-13.png)


## Assets for model customization using AWS SDK
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You can also use the SageMaker AI Python SDK to create assets. See sample code snippet below:

```
from sagemaker.ai_registry.air_constants import REWARD_FUNCTION, REWARD_PROMPT
from sagemaker.ai_registry.dataset import DataSet, CustomizationTechnique
from sagemaker.ai_registry.evaluator import Evaluator

# Creating a dataset example
dataset = DataSet.create(
            name="sdkv3-gen-ds2",
            source="s3://sample-test-bucket/datasets/training-data/jamjee-sft-ds1.jsonl", # or use local filepath as source.
            customization_technique=CustomizationTechnique.SFT
        )

# Refreshes status from hub
dataset.refresh()
pprint(dataset.__dict__)

# Creating an evaluator. Method : Lambda
evaluator = Evaluator.create(
                name = "sdk-new-rf11",
                source="arn:aws:lambda:us-west-2:<>:function:<function-name>8",
                type=REWARD_FUNCTION
        )

# Creating an evaluator. Method : Bring your own code
evaluator = Evaluator.create(
                name = "eval-lambda-test",
                source="/path_to_local/eval_lambda_1.py",
                type = REWARD_FUNCTION
        )

# Optional wait, by default we have wait = True during create call.
evaluator.wait()

evaluator.refresh()
pprint(evaluator)
```