Building a Serverless LLM Pipeline with Amazon Bedrock and SageMaker Fine-Tuning using AWS CDK

Large-language models (LLMs) can support a wide range of use cases such as classification, summaries, etc. However they can require additional customization to incorporate domain-specific knowledge and up-to-date information. In this blog we will build serverless pipelines that fine-tuning LLM Models using Amazon SageMaker, and deploying these models. Using AWS CDK as infrastructure as code, the solution separates training workflow from inference workflow, ensuring the production workloads remain stable and unaffected during model training and update. Additionally, leveraging Amazon AppConfig allows dynamic configuration updates without requiring redeployment. The app is built using Kiro🔥 Architecture Overview The system is composed of two main pipeline as the diagram below: Training/Fine-tuning pipeline: responsible for data preparation, model fine-tuning, evaluation, and approval. Inference pipeline: responsible for serving production request using the approved model. 1. Training pip