Tracking and managing assets used in AI development with Amazon SageMaker AI

This article explains how Amazon SageMaker AI helps teams track and manage assets throughout generative AI model development, addressing challenges in coordinating datasets, evaluators, and model configurations across teams and environments.

• Register and version datasets independently as training data evolves
• Create reusable custom evaluators using Lambda functions for quality and safety checks
• Automatic lineage tracking captures relationships between datasets, models, and evaluators
• View complete model lineage from base foundation model through production deployment
• Integrated MLflow support for experiment tracking and model comparison
• Trace deployed models back to original training data and configurations
• Reproduce successful experiments with exact dataset and evaluator versions

SageMaker AI eliminates manual tracking overhead by automatically capturing end-to-end lineage, enabling reproducibility, governance, and informed production deployment decisions.