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LLM experimentation at scale using Amazon SageMaker Pipelines and MLflow

Large language models (LLMs) have achieved remarkable success in various natural language processing (NLP) tasks, but they may not always generalize well to specific domains or tasks. You may need to customize an LLM to adapt to your unique use case, improving its performance on your specific dataset or task. You can customize the model [ ] Conclusion In this post, we focused on how to run LLM fine-tuning and evaluation experiments at scale using SageMaker Pipelines and MLflow. You can use managed MLflow from SageMaker to compare training parameters and evaluation results to select the best model and deploy that model in SageMaker. We also provided sample code in a GitHub repository that shows the fine-tuning, evaluation, and deployment workflow for a Llama3 model. You can start taking advantage of SageMaker with MLflow for traditional MLOps or to run LLM experimentation at scale. About the Authors Jagdeep Singh Soni is a Senior Partner Solutions Architect at AWS based in the Netherlands. He uses his passion for Generative AI to help customers and partners build GenAI applications using AWS services. Jagdeep has 15 years of experience in innovation, experience engineering, digital transformation, cloud architecture and ML applications. Dr. Sokratis Kartakis is a Principal Machine Learning and Operations Specialist Solutions Architect for Amazon Web Services. Sokratis focuses on enabling enterprise customers to industrialize their ML and generative AI solutions by exploiting AWS services and shaping their operating model, such as MLOps/FMOps/LLMOps foundations, and transformation roadmap using best development practices. He has spent over 15 years inventing, designing, leading, and implementing innovative end-to-end production-level ML and AI solutions in the domains of energy, retail, health, finance, motorsports, and more. Kirit Thadaka is a Senior Product Manager at AWS focused on generative AI experimentation on Amazon SageMaker. Kirit has extensive experience working with customers to build scalable workflows for MLOps to make them more efficient at bringing models to production. Piyush Kadam is a Senior Product Manager for Amazon SageMaker, a fully managed service for generative AI builders. Piyush has extensive experience delivering products that help startups and enterprise customers harness the power of foundation models.

Published: 2024-07-24T19:01:32