Automate optimizing Llama 3.x models with the latest graphical tool for Amazon SageMaker Workflows | AWS Machine Learning Blog

Introduction to Amazon SageMaker Pipelines

Amazon SageMaker Pipelines offer a streamlined workflow orchestration service for generative AI models, eliminating the need for specialized workflow frameworks. This article explores how to enhance productivity and simplify the process of building complex AI/ML pipelines using the visual designer.

Setting Up the Automated LLM Customization Workflow

In this section, we demonstrate the process of setting up an automated workflow to fine-tune Llama 3.x models for generating high-quality financial summaries from SEC filings. By automating the fine-tuning process, you can continuously improve the quality of summaries while maintaining accuracy and reproducibility.

Building the SageMaker Pipeline

The article outlines the steps required to create the pipeline, including accessing the visual editor in the SageMaker Studio console, defining pipeline steps, saving progress, and fine-tuning models from SageMaker JumpStart using the Fine tune step. The pipeline structure and prerequisites for building the solution are detailed.

Evaluating Model Performance and Registering to the Model Registry

After deploying the fine-tuned model to an inference endpoint, the article explains how to evaluate the model’s performance against real-world queries using the Execute code step. Additionally, it covers registering the model to the SageMaker Model Registry based on evaluation scores, with the Fail step configured in case of below-threshold performance.

Executing the Pipeline and Scaling Up

The final section delves into executing the constructed pipeline manually, monitoring its progress, and scaling up pipeline executions using SageMaker APIs and SDK. The article highlights the automated scalability of SageMaker Pipelines based on workload requirements and provides insights on managing infrastructure capacity.

Images will be inserted where available.