Eight new capabilities have been unveiled for Amazon SageMaker, AWS’s end-to-end machine learning (ML) service. Developers, data scientists, and business analysts use Amazon SageMaker to build, train, and deploy ML models quickly and easily using its fully managed infrastructure, tools, and workflows.
The new features include new Amazon SageMaker governance capabilities that provide visibility into model performance throughout the ML lifecycle. New Amazon SageMaker Studio Notebook capabilities provide an enhanced notebook experience that enables customers to inspect and address data-quality issues in just a few clicks, facilitate real-time collaboration across data science teams, and accelerate the process of going from experimentation to production by converting notebook code into automated jobs. Finally, new capabilities within Amazon SageMaker automate model validation and make it easier to work with geospatial data.
