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  • When the big cloud infrastructure providers aren't the right fit

    When a business chooses one of the major providers for its cloud services, there shouldn't be a lot of surprises. You do your homework, consider the available services, select instance types, compare costs and then make a decision. At that point, you should know what you are getting. But what if you don't want what you're getting?

    There are scenarios where a business seeks out the services of one of the specialty cloud infrastructure providers. These niche providers compete against behemoths such as Amazon Web Services, Microsoft Azure and Google Cloud Platform, but not by trying to match them service for service. Nor can they keep pace on price. Instead, these smaller providers focus on particular niches, trying to offer specialized services, customer care and flexibility that the hyperscale providers may struggle to furnish.

    The cover story for this issue of Modern Infrastructure delves into the reasons a business might choose a niche player instead of one of the major cloud infrastructure providers. TechTarget's Kristin Knapp reports on how these situations arise and how businesses respond. The right fit, she finds, might not always be found in one of the expected places.

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  • Population health management platform uses AI, machine learning

    In this Innovation Spotlight SearchHealthIT takes a look at a population health management platform that is utilizing AI and machine learning. Continue Reading

  • Amazon Lex flexes Alexa's machine learning muscles

    Alexa's skills are limited for developers who wanted to add voice recognition capabilities to certain apps. Amazon Lex extends the technology for savvy enterprise IT teams. Continue Reading

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  • How machine learning improves energy consumption

    Machine learning stands to have an incredibly powerful opportunity to transform the way we globally produce and consume energy. Continue Reading

  • Advanced machine learning lends a helping hand to network security

    Advanced machine learning can help distinguish between false alarms and real network threats, creating valuable time for IT employees. But the technology still faces challenges. Continue Reading

  • Advanced analytics tools extract business value from big data

    Big data environments based on technologies such as Hadoop and Spark are being deployed more widely -- and the same goes for advanced analytics tools that can help organizations make effective use of the data flooding into those systems. In fact, predictive analytics software was the top choice for planned business intelligence and analytics investments by respondents to a TechTarget survey.

    And in many cases, deployments of advanced analytics software to support big data applications aren't a one-and-done thing. Macy's uses more than a half-dozen tools to meet different application needs as part of the retailer's big data analytics program. The technology roster includes statistical analysis, predictive modeling and machine learning tools that Macy's couldn't do without. "Because of the volume of data, there's just no humanly possible way to analyze it [manually]," said Seetha Chakrapany, the company's director of marketing analytics and customer relationship management systems.

    Macy's is just one of six organizations featured in this e-book chapter by Executive Editor Craig Stedman. Progressive Casualty Insurance Co. is another. The insurer's data and analytics business leader, Pawan Divakarla, said the capabilities provided by advanced analytics tools are "huge" in enabling Progressive to manage a program for awarding discounts on auto insurance policies to safe drivers based on analysis of operational data collected from their vehicles.

    But there are issues to contend with along the way, from the complexity of developing predictive models and machine learning algorithms to the challenge of sharing analytics results with business executives. Find out how Macy's, Progressive and others have overcome the hurdles and made advanced analytics against pools of big data work successfully.

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