Amazon Web Services has expanded the capabilities of its Amazon SageMaker machine learning toolkit to address a number of challenges that enterprises confront when trying to operationalize machine learning, from model organization, training, and optimization to monitoring the performance of models in production.
Launched at the Amazon’s re:invent conference in 2017, SageMaker aims to make machine learning adoption simpler for customers by bringing together a hosted environment for Jupyter notebooks with built-in model management, automated spin up of training environments in Amazon S3, and HTTPS endpoints for hosting capabilities using EC2 instances.
