AWS Podcast #706: Automate LLM fine-tuning and selection with Amazon SageMaker Pipelines
29 snips
Feb 3, 2025 Join Piyush Kadam, a Senior Product Manager at Amazon SageMaker, and Lauren Mullennex, a Senior AIML Specialist Solutions Architect, as they delve into the fascinating world of LLMOps. They discuss the unique challenges of deploying large language models and explore the latest advancements in SageMaker, particularly focusing on SageMaker Pipelines for automating ML workflows. Discover how features like model evaluation and visual design tools help simplify processes for all users. Plus, learn about cost optimization techniques and enhancing model performance with effective management strategies.
AI Snips
Chapters
Transcript
Episode notes
LLMOps Evolution
- LLMOps has emerged from DevOps and MLOps, adapting to the unique needs of large language models.
- It addresses prompt engineering, evaluation, and responsible AI guidelines.
Key Considerations for LLMOps
- LLMs require customization and evaluation due to their black-box nature and potential biases.
- Responsible AI guidelines necessitate thorough evaluation, especially in sensitive applications.
SageMaker Pipelines for LLMOps
- Leverage SageMaker Pipelines for repeatable and scalable LLM workflows.
- It automates tasks, integrates with SageMaker components, and manages experiment trials.