Amazon Web Services (AWS) recently introduced support for customizing models in Amazon Bedrock, a fully managed service for AI applications. The key role of Amazon Bedrock is to provide high-performing foundation language models from leading AI firms like Cohere and Meta. It aids businesses in using their proprietary data to pre-train their models according to their specific needs.
To assist in these tasks, AWS has launched a native integration of Amazon Bedrock and AWS Step Functions. This enables users to automate workflows for customizing Bedrock models. The post explains how to utilize Step Functions to address significant pain points in model customization, reducing development timelines and fully exploiting its potentials.
The authors present a step-by-step explanation aimed at achieving a seamless workflow that includes model training, evaluation, and monitoring. For exemplification purposes, they use a summarization use case with the Cohere Command Light Model featured in Amazon Bedrock.
The discussion then proceeds with the functionality architecture, prerequisites for initiating the process, preparing the demonstration, running the Step Functions workflow, monitoring progress, assessing the outcome of training the base foundation model, and steps for cleaning up.
Key steps include deploying the solution using AWS’s ‘Serverless Application Model’, uploading proprietary training data to an S3 bucket, running the Step Functions workflow and monitoring its progress, viewing the outcome of training the base model, and cleaning up data and models that are not required.
In conclusion, the authors describe how AWS services such as Amazon Bedrock and Step Functions can facilitate the customization process of large language models by allowing enterprises to concentrate on their unique data and use cases. Improved efficiency in technical management and operational challenges are among the benefits of adopting an automated workflow for customization and evaluation.
