A user can conduct machine learning (ML) data experiments in data environments, such as Snowflake, using the Snowpark library. However, tracking these experiments across diverse environments can be challenging due to the difficulty in maintaining a central repository to monitor experiment metadata, parameters, hyperparameters, models, results, and other pertinent information. In this post, we demonstrate how to integrate Amazon SageMaker managed MLflow as a central repository to log these experiments and provide a unified system for monitoring their progress.
Amazon SageMaker managed MLflow offers fully managed services for experiment tracking, model packaging, and model registry. The SageMaker Model Registry streamlines model versioning and deployment, facilitating seamless transitions from development to production. Additionally, integration with Amazon S3, AWS Glue, and SageMaker Feature Store enhances data management and model traceability. The key benefits of…
