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Scale Computing Aims for the Small Business Market and Sees Exceptional Growth
Scale Computing, founded in 2007, focuses on the small- and medium-sized business market and has driven growth of 50 percent year over year since 2014, according to Alan Conboy of the office of the CTO.
When asked about Scale’s growth, Conboy told Info-Tech in an exclusive briefing that he felt comfortable stating a 50 percent year-over-year growth since 2014, but felt that number was conservative. Conboy noted that the development of their own solution rather than building off someone else’s hypervisor was a major part of this success.
The Scale Hyperconverged Infrastructure would seem to fill a niche that the larger players are leaving largely untouched. Smaller businesses need solutions that are as robust and practical as the larger ones, but those are often priced out of reach for smaller businesses.
With Scale’s solutions, deep-specific expertise is not needed for their platform. This lowers complexity for small IT teams.
Perhaps Scale’s approach is the one needed to address the hole left by the likes of Nutanix and HPE SimpliVity.
How long will it be before the larger vendors take notice of the market available to them in the small and medium business arena?
One thing is certain, though, being the first to concentrate on this space will garner brand loyalty, and as those small companies grow, so will Scale.
Last month Amazon released SageMaker Studio, an IDE for machine learning (ML). The objective for this new-ish offering was to address “immature tooling” in ML and make it easier for data scientists to create and deploy ML models.
So, you know about AI biases but want to see a demonstration of what’s involved in identifying and removing them from a machine learning/AI application? A recent webinar by DataRobot does just that: it walks you through a small ML project and explains step by step what to do and how.
Doctors at the Moorfields Eye Hospital in London have built a data set that links patient retinal scans with nationally held data about people with Alzheimer’s. They plan to use it to see if they can detect the early stages of Alzheimer’s disease from retinal changes using machine learning.
DataRobot, a vendor of enterprise AI, recently released a report revealing that nearly half (42%) of AI professionals in the US and the UK are “very” or “extremely” concerned about AI bias.
Wrike’s Approach to Avoiding Employee Burnout Requires a Collaborative, Top-Down Approach to Portfolio Planning
Wrike’s Laura Quiambao recently blogged about the dangers of employee burnout and highlighted how Wrike Resource can help. It’s tough to argue with her four proposed solutions, but a fifth component is absent from her analysis: engaged, responsible portfolio ownership.
A newly announced collaboration between CipherCloud and Thales promises to enable zero trust access control for data in the cloud. This may be a compelling value proposition for companies looking for a CASB with integrated zero trust identity management.
Aha! has improved its integration with Azure DevOps to improve release and sprint visibility for both developers and stakeholders.
IoT manufacturers aren’t just waiting for 5G connectivity to deliver significant smart city solutions. From a smart city conference in Dublin, we showcase three examples of how IoT is improving urban environments today.
The AI system developed by Google Health more accurately identifies breast cancer than human experts, reports The Guardian (and others). The system has been tested in the UK and the US, and the results are published in Nature, one of the most prestigious scientific journals in the world.