Navigating EU AI Act requirements for LLM fine-tuning on Amazon SageMaker AI | Amazon Web Services
The EU AI Act requires organizations fine-tuning large language models (LLMs) to track computational resources measured in floating-point operations (FLOPs) to determine compliance obligations. As customers increasingly fine-tune LLMs for domain-specific use cases, we hear a common question: how do I know if my training job triggers new regulatory obligations?
Amazon SageMaker AI provides a managed machine learning (ML) service for building, training, and deploying models. This solution uses Amazon SageMaker Training jobs to run fine-tuning workloads on fully managed infrastructure. SageMaker Training jobs handle resource provisioning, scaling, and cluster management, with built-in support for distributed training, integration with AWS CloudTrail and Amazon CloudWatch for governance, and automatic decommissioning of compute resources after training completes. The Fine-Tuning FLOPs Meter extends these capabilities with purpose-built compliance tracking that integrates into your existing SageMaker AI pipelines.
In this post, we show you how to set up FLOPs tracking during LLM fine-tuning using...
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