Accelerate data preparation using Amazon SageMaker Data Wrangler for diabetic patient readmission prediction

By Dustin Ward

AWS FeedAccelerate data preparation using Amazon SageMaker Data Wrangler for diabetic patient readmission prediction Patient readmission to hospital after prior visits for the same disease results in an additional burden on healthcare providers, the health system, and patients. Machine learning (ML) models, if built and trained properly, can help understand reasons for readmission, and predict…

Ingesting PI Historian data to AWS Cloud using AWS IoT Greengrass and PI Web Services

By Dustin Ward

AWS FeedIngesting PI Historian data to AWS Cloud using AWS IoT Greengrass and PI Web Services In process manufacturing, it’s important to fetch real-time data from data historians to support decisions-based analytics. Most manufacturing use cases require real-time data for early identification and mitigation of manufacturing issues. A limited set of commercial off-the-shelf (COTS) tools…

Scalable, Cost-Effective Disaster Recovery in the Cloud

By Dustin Ward

AWS FeedScalable, Cost-Effective Disaster Recovery in the Cloud Should disaster strike, business continuity can require more than just periodic data backups. A full recovery that meets the business’s recovery time objectives (RTOs) must also include the infrastructure, operating systems, applications, and configurations used to process their data. The growing threats of ransomware highlight the need…

AWS Glue FindMatches now provides match scores

By Dustin Ward

AWS FeedAWS Glue FindMatches now provides match scores The FindMatches ML transform in AWS Glue now includes an option to output match scores, which indicate how closely each grouping of records match each other. The FindMatches transform allows you to identify duplicate or matching records in your dataset, even when the records do not have…

Machine learning inference at scale using AWS serverless

By Dustin Ward

AWS FeedMachine learning inference at scale using AWS serverless With the growing adoption of Machine Learning (ML) across industries, there is an increasing demand for faster and easier ways to run ML inference at scale. ML use cases, such as manufacturing defect detection, demand forecasting, fraud surveillance, and many others, involve tens or thousands of…