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  • GPU-accelerated computing makes its way into the data center

    Once viewed narrowly as a gaming technology, GPUs have made their way into enterprise data centers, fueling initiatives around machine learning, artificial intelligence and more. Continue Reading

  • Not playing games: The GPU vs. CPU question gets more interesting

    Putting thousands of cores to work makes perfect sense when you're powering a video game. But graphics processing units, or GPUs, can't serve much practical purpose in a corporate data center, can they?

    The GPU vs. CPU discussion isn't as odd as it sounds. In fact, a more broad application of GPUs in the data center can provide the processing punch that CPUs simply cannot. With tasks where data can be processed in parallel rather than in sequence, GPUs might be a particularly valuable tool. The cover story in this issue of Modern Infrastructure looks at scenarios where simultaneous processing can be just what's needed, such as with machine learning and artificial intelligence.

    This month's Modern Infrastructure also looks at how data can be protected while in the data center. Full encryption is seemingly a perfect defense for data theft. But that's a difficult -- and, to some, an unrealistic-- road to travel. We look at how an IT team can better safeguard its data, factoring in costs, the tools available and the tradeoffs involved in hack-proofing an organization's data.

    And what about the vulnerabilities that arise from within your business? We examine the risk of data loss posed by the shadow IT phenomenon, an ongoing and ever-changing challenge.

    It's clear that data is an increasingly valuable resource. Processing it and keeping it safe -- whether that entails taking up the question of GPU vs. CPU for certain workloads or upgrading encryption -- need to be priorities for IT professionals in organizations of all sizes and types.

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  • What global threat intelligence can and can't do for security programs

    Global threat intelligence is a valuable complement to a company's security program, but it can't replace security measures like training and internally collected data. Continue Reading

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  • 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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  • Machine learning platform minimized Brexit fallout for investors

    The Brexit vote caused an enormous shock to the world's financial systems and cost investors a lot of money. But a data-driven strategy and machine learning tools helped some avoid the risks. Continue Reading

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