Operationalize generative AI applications on AWS: Part II – Architecture Deep Dive

This article discusses an architecture for operationalizing generative AI applications on AWS, with a focus on creating dialogs for non-player characters (NPCs) in games. The key points are:

Specifically, the article covers:

• Base model inference: Invoking foundation models hosted on Amazon Bedrock via APIs (API Gateway, Lambda) for generating text or image content
• Code pipeline: Automating the continuous integration and deployment of the application using AWS CodePipeline, CodeCommit, CodeBuild, and CloudFormation
• Continuous fine-tuning: Automating the process of data preprocessing, model fine-tuning, evaluation, and registration using SageMaker Pipelines, Step Functions, and Bedrock
• Retrieval Augmented Generation (RAG): Adding context to the model's responses by retrieving relevant information from a vector database (Amazon OpenSearch Service) and augmenting the input prompts
• Conclusion: The article provides an overview of the architecture and points to the code and a workshop for further details