Machine Learning Solves Data Center Problems, But Also Creates New Ones

insideBIGDATA | May 02, 2018

Machine Learning Solves Data Center Problems, But Also Creates New Ones
In this special guest feature, Geoff Tudor, VP and GM of Cloud Data Services at Panzura, believes AI poses both opportunities and risks in the automation of the datacenter. This article provides an overview regarding the impact of AI in the datacenter, and how companies can prepare their storage infrastructure for these technologies. Geoff  has over 22 years experience in storage, broadband, and networking. As Chief Cloud Strategist at Hewlett Packard Enterprise, Geoff led CxO engagements for Fortune 100 private cloud opportunities resulting in 10X growth to over $1B in revenues while positioning HPE as the #1 private cloud infrastructure supplier globally. Geoff holds an MBA from The University of Texas at Austin, a BA from Tulane University, and is a patent-holder in satellite communications. Artificial intelligence (AI) with machine learning (ML) capabilities offers the promise of increased efficiency in data centers. As evidence of this, Deloitte Global predicts that the number of ML pilots and implementations will double in 2018 compared to 2017, and double again by 2020. According to McKinsey, total annual investment in AI was between $8B to $12B in 2016.1. AI with ML is particularly important for data centers with 100 or more physical servers because 24×7 support becomes extremely complex with the large number of systems that are managed by multiple people. And, imagine how much more efficient managing data centers that house big data could be if ML could be applied.

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Big data has the potential to enhance, evolve and drive business, but big data adoption must be carefully planned and executed in order to be effective. The graphic below highlights 5 crucial factors that all organizations should take into account before selecting a big data vendor.


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