ClearScale Blog

With the growing volume of large data sets, low-cost data storage, and more powerful data processing capabilities, the applications of machine learning (ML) are growing exponentially. But many organizations are still reluctant to take the ML plunge. And, for those that do, the success rate for ML projects is worringly low. (Gartner notes that up to 85% of ML projects ultimately fail to deliver on their intended promises to business.) So, what can companies do to achieve a higher success

The hype about machine learning (ML) is well deserved. It’s not just making things easier for the companies that are taking advantage of it. It’s changing the way they do business for the better. For example, ML is: Being used by financial institutions to quickly detect fraudulent activity Enabling healthcare practitioners diagnose diseases and prescribe appropriate treatments more effectively Helping manufacturing companies monitor equipment so issues can be dealt with before they disrupt operations Allowing streaming services to

For years, Amazon Web Services (AWS) has been at the forefront of developing innovative cloud technologies like Artificial Intelligence (AI) and machine learning services. To help modern businesses adapt to continuous market changes, AWS frequently releases new AI and machine learning services that make users’ lives easier by reducing the amount of time spent on routine, manual, and error-prone operations. In 2017, AWS changed the status quo by releasing AWS Glue - the first-ever serverless Spark offering, that makes it

In the application development field, there are machine learning (ML) apps and there are cloud-native ML apps. ML apps are just what the name indicates: apps that incorporate ML. The cloud-native variety refers to ML applications that, in addition to their machine learning functions, are designed to exploit the benefits of the cloud. That includes its elasticity and flexible storage capabilities. ML solutions can be developed using open-source frameworks, such as TensorFlow and CNTK, that run on in-house hardware. Or

Why Use Machine Learning? While many industries are struggling amid the Coronavirus pandemic, both the IT industry and the broader trend of transition to remote work have revealed many areas where traditional approaches to managing businesses create unnecessary waste. Still, data science and its subdivision - machine learning - reveal that such expansion is nearly limitless. According to Fortune Business Insights, the machine learning market was valued at $27B in 2019 with a projected value of $267B by 2027. This

Interest in machine learning (ML) application development is increasing as organizations see the benefits ML offers. It’s enabling rapid innovation, driving efficiencies, and helping to meet customer needs at an unprecedented pace and scale. The problem is that many businesses lack the resources — both the technical and the human kind — to develop ML apps. In part 3 of our 3-part blog series on ML, we discuss how the cloud and cloud-native app development is changing that. The Resource Challenge

It seems like every day there are new acronyms and buzzwords joining the application development lexicon. That doesn’t mean they’re just passing fads. Most represent advances and technologies that are impacting application development in a positive way. That’s the case with MLOps, which fuses the words “machine learning” and “operations” — and is the focus of part 2 in our three-part blog series on machine learning (ML). The Need for MLOps What is MLOps? Integrating machine learning, DevOps

From fraud detection and email filtering to self-driving cars and patient diagnosis, the potential applications of machine learning seem almost endless. But if you’re interested in deploying a machine learning (ML) application, where do you begin? Do you build it in-house or outsource it? What resources are required? What platform should you build it on? In the first of this three-blog series, we cover some of the key considerations for developing and deploying ML applications. The Basics of Machine

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