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Introduction
Large language models have revolutionized the way businesses generate intelligent and nuanced responses across various use cases. However, enterprises often have unique data and requirements that necessitate customizing these models beyond their out-of-the-box capabilities. Amazon Bedrock, a fully managed service, offers a range of high-performing foundation models from leading AI companies, along with a single API, to build generative AI applications with security, privacy, and responsible AI.
Customizing Foundation Models with AWS Step Functions
AWS has announced support for customizing foundation models in Amazon Bedrock, enabling customers to pre-train selected models using their proprietary data to tailor model responses to their business context. To achieve this, AWS has integrated Amazon Bedrock with AWS Step Functions, a serverless workflow orchestration service. This integration empowers customers to automate the process of customizing and evaluating foundation models, streamlining their workflows and reducing development timelines.
Architecture
To customize a foundation model in Amazon Bedrock, we will use a summarization use case with the Cohere Command Light Model. The architecture consists of several components:
* A Step Functions state machine that orchestrates the customization and evaluation of the foundation model
* Amazon Bedrock APIs for creating and managing the customization job
* An Amazon S3 bucket for storing the training data and output files
* An AWS Lambda function for evaluating the quality of the customized model
Prerequisites
Before proceeding, ensure you have the following:
* An AWS account
* Access to the AWS Management Console and AWS CLI
* Git installed
* AWS SAM installed
* Docker installed and running
* Cohere Command Light Model access enabled in the Amazon Bedrock console in the specified region
Demo Preparation
To implement the model customization workflow, follow these steps:
1. Deploy the solution using the AWS SAM template
2. Upload proprietary training data to the S3 bucket
3. Run the Step Functions workflow and monitor
4. View the outcome of training the base foundation model
5. Clean up
Step 1: Deploy the Solution using the AWS SAM Template
Refer to the GitHub repository for the latest instructions. Run the following commands to deploy the Step Functions workflow using the AWS SAM template:
“`bash
git clone https://github.com/aws-samples/amazon-bedrock-model-customization.git
cd amazon-bedrock-model-customization
build.sh
aws sam deploy –guided
“`
Provide the required parameters when prompted.
Step 2: Upload Proprietary Training Data to the S3 Bucket
Upload the training data files to the S3 bucket using the following command:
“`bash
aws s3 cp validation-data.json s3://{ValidationDataBucket}/validation-data.json –region {your-region}
“`
Replace `{ValidationDataBucket}` with the value from the `sam deploy –guided` output.
Step 3: Run the Step Functions Workflow and Monitor
Run the following AWS CLI command to start the Step Functions workflow:
“`bash
aws stepfunctions start-execution –state-machine-arn “{StateMachineCustomizeBedrockModelArn}” –input “{“BaseModelIdentifier”: “cohere.command-light-text-v14:7:4k”,”CustomModelName”: “{UniqueModelName}”,”JobName”: “{UniqueJobName}”, “HyperParameters”: {“evalPercentage”: “20.0”, “epochCount”: “1”, “batchSize”: “8”, “earlyStoppingPatience”: “6”, “earlyStoppingThreshold”: “0.01”, “learningRate”: “0.00001”},”TrainingDataFileName”: “training-data.jsonl”}” –region {your-region}
“`
Replace `{StateMachineCustomizeBedrockModelArn}` with the value from the `sam deploy –guided` output.
Step 4: View the Outcome of Training the Base Foundation Model
After the Step Functions workflow completes successfully, you will receive an email with the outcome of the quality of the customized model. If the customized model isn’t performing better than the base model, the provisioned throughput will be deleted.
Step 5: Clean Up
Delete the Amazon Bedrock provisioned throughput and custom model using the following commands:
“`bash
aws bedrock delete-provisioned-model-throughput –provisioned-model-id {ProvisionedModelArn} –region {your-region}
aws bedrock delete-custom-model –model-identifier {CustomModelName} –region {your-region}
“`
Replace `{ProvisionedModelArn}` and `{CustomModelName}` with the values from the previous steps.
Conclusion
In this post, we demonstrated how to customize a foundation model in Amazon Bedrock using AWS Step Functions as the orchestration engine. By automating the process of customization and evaluation, customers can streamline their workflows and reduce development timelines. With the power of AWS, businesses can unlock the full potential of large language models to drive innovation and growth.
Frequently Asked Questions
Q1: What is Amazon Bedrock?
Amazon Bedrock is a fully managed service that offers a range of high-performing foundation models from leading AI companies, along with a single API, to build generative AI applications with security, privacy, and responsible AI.
Q2: What is AWS Step Functions?
AWS Step Functions is a serverless workflow orchestration service that enables customers to automate complex workflows and processes.
Q3: What is the benefit of customizing foundation models in Amazon Bedrock?
Customizing foundation models in Amazon Bedrock enables customers to tailor model responses to their unique business context and requirements, leading to more accurate and relevant results.
Q4: How do I deploy the Step Functions workflow using the AWS SAM template?
Deploy the Step Functions workflow using the AWS SAM template by running the following commands:
“`bash
git clone https://github.com/aws-samples/amazon-bedrock-model-customization.git
cd amazon-bedrock-model-customization
build.sh
aws sam deploy –guided
“`
Provide the required parameters when prompted.
Q5: What is the output of the Step Functions workflow?
The output of the Step Functions workflow is an email with the outcome of the quality of the customized model. If the customized model isn’t performing better than the base model, the provisioned throughput will be deleted.
