Tracking and Managing Assets Used in AI Development with Amazon SageMaker AI
Introduction As enterprises accelerate the adoption of artificial intelligence, the complexity of managing AI assets grows rapidly. Modern ML workflows involve multiple datasets, feature transformations, training jobs, experiments, model versions, evaluation metrics, and deployment endpoints. Without a structured approach to track and govern these assets, organizations face challenges related to reproducibility, compliance, collaboration, and operational…