ClearScale Blog

From sales forecasting to virtual personal assistants, machine learning (ML) has become integral to many common business processes and applications. Its power lies in enabling organizations to identify trends and patterns — many of which are imperceptible or difficult to detect by people — from large, diverse data sets. In addition, ML helps automate data analysis, significantly reducing times and costs. It’s also less error-prone, and enables organizations to deliver more personalized services and differentiated products. However, using the technology has

Medical coding plays a key role in the healthcare industry. It enables both providers and payors to describe diagnoses and treatments, and to determine the associated costs and reimbursements. It’s also complicated, time-consuming, and prone to errors. A recent collaboration between ClearScale, AWS, and Creative Practice Solutions, a medical consulting firm, has yielded an application prototype to help overcome those issues. Powered by AWS artificial intelligence (AI) and machine learning (ML) services, the app translates recorded medical appointment notes

AWS re:Invent 2019 demonstrated once again that AWS has been busy. The company announced 77 product launches, feature releases, and services at its annual conference — which are all, at some level, driving IT evolution and business transformation. Here are five that we think are going to prove to be extremely beneficial: 1. Amazon Redshift Updates AWS made three notable updates to Redshift. First up, it’s now possible to unload an Amazon Redshift query result to an Amazon S3

The applications for matchmaking powered by artificial intelligence (AI) go far beyond online dating. The use of machine learning (ML) can help match job seekers to jobs, startups to venture capitalists, nonprofit organizations to grants, and even prospective college students to institutions of higher education. However, it’s not just the match that matters. AI offers opportunities to do more with the data gathered for these applications. For example, the data gathered for matching students to colleges and universities could

Read or watch the news on any given day and there will undoubtedly be an announcement that a high-profile data breach has occurred in a heavily used financial or healthcare organization, website, or social media channel. As unfortunate as these incidents are, most are preventable with proper security policies, active data breach monitoring, and audit logs that can track any breach before it becomes an issue. Not all data breaches are due to outside attackers launching attacks or ransomware heists

Any business operating today is familiar with the need to provide support to their customers. Typically, this involves a call center solution when a product or service is first rolled out and, over time, grows to include support tools like online forums or knowledge bases to help manage the increased complexity to support a growing customer or product base. Although technologies continue to advance and changes have been made to how support centers operate, the costs to maintain or sustain

Higher education institutions have for years faced the challenge of trying to not only identify prospective students, but optimizing the recruitment and retention processes so as to not spend resources on prospective students that ultimately decide not to attend the college or university. Schools typically track information about the ultimate outcome of a student deciding to attend or not, and this information has been used to help staff determine the proper approach to minimizing marketing spend where appropriate. One organization

With the ever-increasing breadth of information surrounding your customers’ demographic and financial transaction history, the amount of associated data can grow very large, very quickly. From how they use their credit cards, to what vehicles they purchase/lease and when, to how often they move, rent, or buy a place to live, and even how many times they use checks over debit or credit cards, consumers’ financial transactional footprints are varied and considerable. This large volume of information can be