ClearScale Blog

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

Machine learning-enabled medical image analysis. Prosthetics customized for individual patients courtesy of 3D printing. Virtual Reality (VR) tools that enable medical students to learn from life-and-death scenarios in low-stakes environments. These are just some of the numerous examples of technology pushing healthcare to new levels of sophistication. In many cases, the technologies driving these innovations are cloud-based. Many healthcare organizations are eager to take advantage of the well-documented benefits of the cloud. However, as these organizations move to the cloud

With the advent of the cloud, and a push by organizations around the world to off-load their IT infrastructures from data centers into the cloud, the evolution of software development paradigms shifted. Organizations are looking for solutions for higher availability, increased redundancy, and shorter development pipelines for their software products. This is being driven by their own customer bases demanding continuously updated software. In the past, this type of challenge would be difficult for even the most nimble development group

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

Today, there are numerous advantages to migrating and modernizing Microsoft Windows workloads on the cloud. Companies that plan ahead and execute well can improve performance significantly while also reducing costs and overhead. In addition, industry-leading public cloud providers like Amazon Web Services (AWS) now offer a multitude of fully managed solutions and tools to help organizations elevate their IT. In other words, companies are running out of reasons to stick with legacy tech. Below, we describe the top five benefits

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