Building custom foundation models requires coordinating multiple assets across the development lifecycle such as data assets, compute infrastructure, model architecture and frameworks, lineage, and production deployments. Data scientists create and refine training datasets, develop custom evaluators to assess model quality and safety, and iterate through fine-tuning configurations to optimize performance. As these workflows scale across teams and environments, tracking which specific dataset versions, evaluator configurations, and hyperparameters produced each model becomes challenging. Teams often rely on manual documentation in notebooks or spreadsheets, making it difficult to reproduce successful experiments or understand the lineage of production models.
This challenge intensifies in enterprise environments with multiple AWS accounts for development, staging, and production. As models move through deployment pipelines, maintaining visibility into their training…
