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Machine learning News

  • October 19, 2017 19 Oct'17

    What to expect from AppDynamics Summit 2017

    First the Earth cooled... and then there were tools, devices, machines and eventually software applications - then, finally, we started calling them apps. In the second age of man (and woman and ...

  • October 17, 2017 17 Oct'17

    CIOs should lean on AI 'giants' for machine learning strategy

    Components of AI, such as machine and deep learning, will be part and parcel of every enterprise. When devising a machine learning strategy, CIOs should think of it as the next wave of analytics.

  • October 13, 2017 13 Oct'17

    Feature engineering headache disappears with deep learning

    NEW YORK -- One of the biggest differences between machine learning and deep learning is the effort that goes into making the algorithms work. With machine learning, data scientists have to perform ...

  • October 13, 2017 13 Oct'17

    Arkadin unveils Cisco integrations for cloud telephony

    In UC news, Arkadin announces integrations with Cisco Spark and WebEx that support cloud-based telephony, while Box unveils machine learning tools to digitize and automate workflows.

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  • Managing the big data ecosystem requires agility amid disruptions

    In some regards, the term big data management can be viewed as an oxymoron. In fact, oxymorons abound in this industry and society -- virtual reality, artificial intelligence, science fiction and awfully good, the latter of which can apply to the challenges encountered in managing the onslaught of big data from multiple sources. There are countless tools, techniques and practices available for the big data ecosystem to properly gather, mine, prep, store and analyze data and help smooth operations, build marketing campaigns, improve customer service and develop the next new product disruptor. As simplistic as this may sound, it's up to data managers to sort it all out as their data lakes swell beyond capacity.

    "The data lake isn't where data goes to die," Gartner analyst Merv Adrian said at the 2017 Pacific Northwest BI Summit, "it's where data goes to live."

    October's Business Information opens with our editor's note and advice for data managers to move beyond traditional data control to the critical task of improving data quality and delivery -- taking all that raw data and making it useful. Whether for internal or external business use, the demands for instantaneous data access continue to accelerate, spurred on by mobile apps, artificial intelligence (AI), machine learning and internet of things (IoT).

    In that vein, our cover story examines companies that use their big data ecosystem to divert data lakes toward developing new strategies, products and revenue streams -- in the process, smashing their old business patterns. In another feature, IoT and machine learning technologies help take the guesswork out of estimated times of arrival for transport companies whose businesses depend on shipping and receiving goods.

    Also in this issue, a business intelligence project combined three data warehouses into one to reduce warehouse size by 80% and data load time from several weeks to just days while causing IT staffing problems in the process. In other features, learn how metadata programs can ease mega management woes; semantic technology could be a blessing or curse to AI; companies are gearing up for greater big data management deployments; and all data must be treated equally in the search to find value.

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  • Machine learning, IoT bring big changes to data management systems

    Companies are using big data systems, deep learning and machine learning techniques to drive software advances. To go even further, their data management systems must also evolve. Continue Reading

  • Predictive logistics reach beyond supply chain visibility

    Getting real-time information on where goods are in a supply chain is commonplace with sensors and big data, but some firms use machine learning to predict more accurate ETAs. Continue Reading

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