Implementing model personalization in Amazon Bedrock using AWS Step Functions workflow | AWS Machine Learning Blog

Introduction:
Large Language Models in Business Context

Large language models have become essential for various business use cases, requiring customization beyond their out-of-the-box capabilities. Amazon Bedrock offers a managed service with high-performing foundation models and robust capabilities for generative AI applications.

Customizing Models with Amazon Bedrock

Amazon Web Services (AWS) now supports customizing models in Amazon Bedrock, enabling customers to pre-train selected models to tailor responses to their unique needs. The quality of custom models relies on training data quality and hyperparameters, leading to multiple iterations for optimal customization.

Automating Customization Workflows

AWS introduced native integration between Amazon Bedrock and AWS Step Functions to orchestrate repeatable and automated workflows for model customization. By configuring workflows for training, evaluation, and monitoring, development timelines are reduced, maximizing the value of Amazon Bedrock for diverse requirements.

Implementing Model Customization Workflow

This paragraph will guide you through the phases of implementing a model customization workflow using Step Functions. It involves deploying the workflow with the AWS SAM template, configuring the training data in JSON Line format, starting the Step Functions state machine, and receiving notifications on the quality and performance of the customized model.

Post-Workflow Tasks and Best Practices

After completing the Step Functions workflow and evaluating the customized model, it’s essential to decommission provisioned AWS resources effectively to optimize costs and enhance security. Proper decommissioning practices ensure the efficient handling of infrastructure components post demonstrations and proofs of concept.

Conclusion

Customizing large language models with Amazon Bedrock and AWS Step Functions streamlines the process for enterprises, enabling quicker adaptations to unique data and use cases. The automated workflow, coupled with email notifications on training outcomes, enhances the efficiency of customizing models while abstracting complexities associated with machine learning expertise and infrastructure. Biswanath Mukherjee, Senior Solutions Architect at Amazon Web Services, collaborates with customers to leverage AWS’s capabilities for innovative solutions.