Fine tune a generative AI application for Amazon Bedrock using Amazon SageMaker Pipeline decorators

This article discusses how to fine-tune a generative AI application for Amazon Bedrock using Amazon SageMaker Pipeline decorators. It provides a solution for building and managing deployment pipelines for generative AI applications at scale, which can be challenging due to the complexities and unique requirements of these systems.

Specifically, the article covers:

• Introduction to SageMaker Pipelines and the benefits of using them for managing generative AI model deployments
• A step-by-step process to convert Python code for fine-tuning an Amazon Bedrock model into a SageMaker pipeline using the @step decorator
• Key steps involved, including data loading, preprocessing, training the model, creating provisioned throughput, and testing the model
• Creating and running the SageMaker pipeline, and tracking the lineage of the pipeline execution in SageMaker Studio
• Conclusion highlighting the advantages of using SageMaker Pipelines for streamlining and automating generative AI model workflows