Transforming Generative AI Workflows with Amazon SageMaker Pipelines
Are you ready to revolutionize your generative AI workflow? With the visual designer for Amazon SageMaker Pipelines, you can now create an end-to-end workflow to train, fine-tune, evaluate, register, and deploy generative AI models seamlessly. SageMaker Pipelines, a serverless workflow orchestration service, is designed specifically for foundation model operations (FMOps), making your generative AI journey from prototype to production a breeze. No specialized workflow frameworks required!
Whether you’re new to pipelines or an experienced user, this step-by-step guide will show you how to leverage the visual designer to boost your productivity and simplify the process of building complex AI and machine learning (AI/ML) pipelines.
Llama Fine-Tuning Pipeline Overview
Let’s dive into setting up an automated LLM customization (fine-tuning) workflow for the Llama 3.x models from Meta, tailored to provide high-quality summaries of SEC filings for financial applications. Fine-tuning LLMs allows you to configure them for improved performance on domain-specific tasks. By automating the fine-tuning process with SageMaker Pipelines, you can ensure your models are always up-to-date with the latest real-world data, enhancing the quality of financial summaries produced by the LLM over time.
Are you ready to create your pipeline? Here’s a quick overview of the steps:
- Fine-tune a Meta Llama 3 8B model using the SEC financial dataset
- Prepare the fine-tuned Llama 3 8B model for deployment
- Deploy the fine-tuned Llama 3 8B model to SageMaker Inference
- Evaluate the performance of the fine-tuned model using the Foundation Model Evaluations (fmeval) library
- Use a condition step to determine if the fine-tuned model meets desired performance criteria
Prerequisites
Before getting started, make sure you have the following prerequisites:
- An AWS account for managing resources
- An IAM role with access to SageMaker
- Access to SageMaker Studio and required instances for endpoint usage and model training
Accessing the Visual Editor
Unlock the power of the visual editor by navigating to Pipelines in SageMaker Studio and selecting Create in visual editor. Explore a variety of step types supported by the visual editor for building your pipeline seamlessly.
Step #1: Fine Tune the LLM
Enhance your LLM with the Fine tune step in the visual editor – select the model, dataset, and instance, leaving room for hyperparameter adjustments based on your needs.
Step #2: Prepare the Fine-Tuned LLM for Deployment
Create a model definition for deployment, ensuring the inclusion of model artifacts and Docker container specifications.
Step #3: Deploy the Fine-Tuned LLM
Deploy your fine-tuned LLM to a real-time inference endpoint, customizing the endpoint type to meet your requirements.
Step #4: Evaluate the Fine-Tuned LLM
Evaluate the performance of your LLM using the Execute code step, running model evaluation code to test its real-world queries response. Dive into defining LLM evaluation logic and uploading it to the visual editor.
Step #5: Condition Step
Set up a condition step to automatically register the fine-tuned model to the SageMaker Model Registry or fail the pipeline execution based on performance thresholds.
Step #6: Register the Model
Configure the Register model step to include the S3 URI and image URI for model registration in the Model Registry.
Step #7: Fail Step
Configure the Fail step to handle cases where the model fails to be registered, ensuring proper messaging for failure scenarios.
Save and Execute the Pipeline
Ready to witness your pipeline in action? Execute the pipeline and monitor its progress as it efficiently scales to meet your AI workflow demands.
Conclusion
Unlock the full potential of generative AI with Amazon SageMaker Pipelines and the visual designer. Empower your AI/ML workflows with streamlined automation and detailed evaluation, ensuring peak performance for your models. Try it out today and experience the future of AI workflow management!
About the Authors
Lauren Mullennex – Senior AI/ML Specialist Solutions Architect at AWS
Brock Wade – Software Engineer for Amazon SageMaker
Piyush Kadam – Product Manager for Amazon SageMaker