Automated model refresh with streaming data

In today's world, being able to quickly bring on-premises machine learning (ML) models to the cloud is an integral part of any cloud migration journey. This post provides a step-by-step guide for launching a solution that facilitates the migration journey for large-scale ML workflows. This solution was developed by the Amazon ML Solutions Lab for customers with streaming data applications (e.g., predictive maintenance, fleet management, autonomous driving). Some of the AWS services used in this solution include Amazon SageMaker, which is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy ML models quickly, and Amazon Kinesis, which helps with real-time data ingestion at scale. Being able to automatically refresh ML models with new data can be of high value to any business when an ML model drifts.