Amazon Web Services on Wednesday added new features to its managed machine learning service Amazon SageMaker, designed to improve governance attributes within the service and adding new capabilities to its notebooks.
Notebooks in context of Amazon SageMaker are compute instances that run the Jupyter Notebook application.
AWS said the new features will allow enterprises to scale governance across their ML model lifecycle. As the number of machine learning models increases, it can get challenging for enterprises to manage the task of setting privilege access controls and establishing governance processes to document model information, such as input data sets, training environment information, model-use description, and risk rating.
