Category: Analytics

Leveraging Analytics and Machine Learning Tools for Readmissions Prediction This blog post was co-authored by Ujjwal Ratan, a senior AI/ML solutions architect on the global life sciences team. In Part 1, we looked at various options to ingest and store sensitive healthcare data using AWS. The post described our shared responsibility model and provided a […]

This blog post was co-authored by Ujjwal Ratan, a senior AI/ML solutions architect on the global life sciences team. Healthcare data is generated at an ever-increasing rate and is predicted to reach 35 zettabytes by 2020. Being able to cost-effectively and securely manage this data whether for patient care, research or legal reasons is increasingly […]

Centralized logging is often required in large enterprise environments for a number of reasons, ranging from compliance and security to analytics and application-specific needs. I’ve seen that in a multi-account environment, whether the accounts belong to the same line of business or multiple business units, collecting logs in a central, dedicated logging account is an […]

Microservice-application requirements have changed dramatically in recent years. These days, applications operate with petabytes of data, need almost 100% uptime, and end users expect sub-second response times. Typical N-tier applications can’t deliver on these requirements. Reactive Manifesto, published in 2014, describes the essential characteristics of reactive systems including: responsiveness, resiliency, elasticity, and being message driven. […]