Using model attributes to track your training runs on Amazon SageMaker Amazon Web Services

With a few clicks in the Amazon SageMaker console or a few one-line API calls, you can now quickly search, filter, and sort your machine learning (ML) experiments using key model attributes, such as hyperparameter values and accuracy metrics, to help you more quickly identify the best models for your use case and get to production faster. The new Amazon SageMaker model tracking capability is available through both the console and AWS SDKs in all available AWS Regions, at no additional charge. Developing an ML model requires experimenting with different combinations of data, algorithm, and parameters--all the while evaluating the impact of small, incremental changes on performance and accuracy. This iterative fine-tuning exercise often leads to data explosion, with hundreds or sometimes thousands of experiments spread across many versions of a model. Managing these experiments can significantly slow down the discovery of a solution.