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Big Data Management

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  • Customer Support through Natural Language Processing and Machine Learning
    Customer Support through Natural Language Processing and Machine Learning Robin Marcenac, Sr. Managing Consultant, IBM, Ross Ackerman, Dir. Digital Support Strategy, NetApp, Alex McDonald, SNIA CSI Recorded: Feb 22 2018 60 mins
    Watson is a computer system capable of answering questions posed in natural language. Watson was named after IBM's first CEO, Thomas J. Watson. The computer system was specifically developed to answer questions on the quiz show Jeopardy! (where it beat its human competitors) and was then used in commercial applications, the first of which was helping with lung cancer treatment.

    NetApp is now using IBM Watson in Elio, a virtual support assistant that responds to queries in natural language. Elio is built using Watson’s cognitive computing capabilities. These enable Elio to analyze unstructured data by using natural language processing to understand grammar and context, understand complex questions, and evaluate all possible meanings to determine what is being asked. Elio then reasons and identifies the best answers to questions with help from experts who monitor the quality of answers and continue to train Elio on more subjects.

    Elio and Watson represent an innovative and novel use of large quantities of unstructured data to help solve problems, on average, four times faster than traditional methods. Join us at this webcast, where we’ll discuss:

    •The challenges of utilizing large quantities of valuable yet unstructured data
    •How Watson and Elio continuously learn as more data arrives, and navigates an ever growing volume of technical information
    •How Watson understands customer language and provides understandable responses

    Learn how these new and exciting technologies are changing the way we look at and interact with large volumes of traditionally hard-to-analyze data.

    After the webcast, check-out the Q&A blog http://www.sniacloud.com/?p=296
  • Top BI Trends for 2018
    Top BI Trends for 2018 Dan Sommer Senior Director, Market Intelligence Lead at Qlik Recorded: Feb 13 2018 61 mins
    It can be hard to keep up with the rapidly changing BI landscape. But it doesn't have to be. Reserve your spot at Qlik's annual BI Trends Webinar.

    In this global webinar live replay, we’ll reveal the top BI Trends for the coming year and how they can help you transform your data. Join Qlik’s Global Market Intelligence lead and former Gartner analyst Dan Sommer to learn why 2018 is the year for the “desilofication of data.”

    Recent events like the Equifax data leak and new regulations like the EU's General Data Protection Regulation have increased the urgency for further change in the BI landscape and to move data out of silos.

    What is the right strategy and framework?
    How can you easily move from "all data," to "combinations of data," to "data insights"?
    Can data literacy and augmented intelligence create a data-driven culture?
    The volume of data available to decision makers continues to be massive, and is growing faster than our ability to consume it. Learn how to move your data out of silos and turn your data into insights.
  • RIDE Containerized Data Science IDE server For Enterprise
    RIDE Containerized Data Science IDE server For Enterprise Ali Marami, Data Science Advisor at R-Brain Recorded: Dec 14 2017 45 mins
    RIDE is an all-in-one, multi-user, multi-tenant, secure and scalable platform for developing and sharing Data Science and Analytics, Machine Learning (ML) and Artificial Intelligence (AI) solutions in R, Python and SQL.

    RIDE supports developing in notebooks, editor, RMarkdown, shiny app, Bokeh and other frameworks. Supported by R-Brain’s optimized kernels, R and Python 3 have full language support, IntelliSense, debugger and data view. Autocomplete and content assistant are available for SQL and Python 2 kernels. Spark (standalone) and Tesnsorflow images are also provided.

    Using Docker in managing workspaces, this platform provides an enhanced secure and stable development environment for users with a powerful admin control for controlling resources and level of access including memory usage, CPU usage, and Idle time.

    The latest stable version of IDE is always available for all users without any need of upgrading or additional DevOps work. R-Brain also delivers customized development environment for organizations who are able to set up their own Docker registry to use their customized images.

    The RIDE Platform is a turnkey solution that increases efficiency in your data science projects by enabling data science teams to work collaboratively without a need to switch between tools. Explore and visualize data, share analyses, all in one IDE with root access, connection to git repositories and databases.
  • Enterprise Analytics Journey, the IBM point of view for IBM Z customers
    Enterprise Analytics Journey, the IBM point of view for IBM Z customers Hélène Lyon, IBM, Distinguished Engineer, IBM Z Solutions Architect Recorded: Dec 14 2017 41 mins
    IT is a key player in the digital and cognitive transformation of business processes delivering solutions for improved business value with analytics. This session will step by step explain the journey to secure production while adopting new analytics technologies leveraging mainframe core business assets
  • Ep. 3 Big Data, Artificial Intelligence & Machine Learning: Q&T SIG Talk
    Ep. 3 Big Data, Artificial Intelligence & Machine Learning: Q&T SIG Talk Todd DeCapua Recorded: Dec 12 2017 20 mins
    Big Data, Artificial Intelligence and Machine Learning

    We will discuss how Big Data, Artificial Intelligence and Machine learning are rapidly impacting businesses and customers, enabling another massive shift through technology enablement. Todd DeCapua will share how these capabilities are being leveraged in Performance Engineering now, and into the future.

    Join us for the next Quality & Testing SIG Talk on Tuesday, January 9, 2018: http://www.vivit-worldwide.org/events/EventDetails.aspx?id=1041157&group=.
  • From Big Data to AI: Building Machine Learning Applications
    From Big Data to AI: Building Machine Learning Applications Maloy Manna Data engineering PM, AXA Data Innovation Lab Recorded: Dec 12 2017 49 mins
    The newest buzzword after Big Data is AI. From Google search to Facebook messenger bots, AI is also everywhere.

    Machine learning has gone mainstream. Organizations are trying to build competitive advantage with AI and Big Data.

    But, what does it take to build Machine Learning applications? Beyond the unicorn data scientists and PhDs, how do you build on your big data architecture and apply Machine Learning to what you do?

    This talk will discuss technical options to implement machine learning on big data architectures and how to move forward.
  • Applying Machine Learning to unstructured files and data for research
    Applying Machine Learning to unstructured files and data for research Dr Tom Parsons, Research Director and Dr Stuart Bowe, Data Scientist from Spotlight Data Recorded: Dec 12 2017 45 mins
    Researchers generate huge amounts of valuable unstructured data and articles from research every day. The potential for this information is huge: cancer and pharmaceutical breakthroughs, advances in technology and cultural research that can improve the world we live in.

    This webinar discusses how text mining and Machine Learning can be used to make connections across this broad range of files and help drive innovation and research. We discuss using Kubernetes microservices to analyse the data and then applying Machine Learning and graph databases to simplify the reuse of the data.
  • From Data with Love: How the data economy is impacting the insurance sector
    From Data with Love: How the data economy is impacting the insurance sector JS Gourevitch, Luca Schnettler, Petra Wildermann, Anil Celik, Thomas Lethenborg Recorded: Nov 20 2017 60 mins
    The data economy and digital technologies are deeply transforming almost all areas of our lives. One of the most heavily transformed revolve around insurance and healthcare with a number of really interesting development possibly redefining the way we take care of ourselves and the way we consumer and use insurance as well.

    From harnessing the power of data to better help mental health patients, carers and medical personnel with their treatments to assessing the risk of developing broad range of illnesses and engaging better with users to propose them personalised healthy life plans to using big data and analytics to track down and prepare for epidemics to using data to better cover cars and drivers with car insurances and finally using social media data for insurers to better engage with customers, this webinar will propose a fascinating exploration of the opportunities, risks, new models supporting the digital transformation in banking.

    Moderated by Jean-Stéphane Gourévitch
    Luca Schnettler, CEO and Founder, HealthyHealth (UK)
    Petra Wildermann, Business Development Director, Metabiota (Switzerland)
    Anil Celik, Co-founder and CEO Urbanstats (US)
    Thomas Lethenborg, CEO, Monsenso (Denmark)
  • Associative Difference: Augmented intelligence across all your data
    Associative Difference: Augmented intelligence across all your data Robert Fleming, Vice-President of International Marketing and Global Campaigns, Qlik Recorded: Nov 15 2017 69 mins
    It's important to be aware of and respond to the key trends in Data Visualization now that we're heading into 2018.

    Join this video panel, where you will learn:

    -How to augment your intelligence across all your data, people and ideas
    -Uncover and take advantage of new data sources
    -Adjusting to the shift towards real-time enterprise
    -Deployment of new workloads like IoT towards the cloud
    -Why on-remise is still the predominant deployment model
    -Democratising access to data

    •Anthony Deighton, Chief Technology Officer and Senior Vice President, Products, Qlik
    •Helena Schwenk, Big Data and Analytics Research Manager, IDC
    •Stephen Line, Regional Vice-President, North EMEA, Cloudera
    •Mike Prorock, CTO and Founder, mesur.io
    •Eduardo Di Monte, CEO of OYLO Trust Engineering, Cybersecurity Advisor for the Utilities sector.
    •Geertjan Woltjes, Chief Operations Officer, Quooker
  • Three Ways To Accelerate Your Data Lake Migration To Cloud
    Three Ways To Accelerate Your Data Lake Migration To Cloud Kelly Stirman, VP Strategy, Dremio Recorded: Oct 25 2017 45 mins
    Public cloud deployments have become irresistible in terms of flexibility, low barriers to entry, security, and developer friendliness. But the sheer inertia of traditional data lakes make them difficult to transition to cloud. In this talk we'll look at examples of how leading companies have made the transition using open source technologies and hybrid strategies.

    Instead of following a "lift and shift" strategy for moving data lake workloads to the cloud, there are new considerations unique to cloud that should be considered alongside traditional approaches related to compute (eg, GPU, FPGA), storage (object store vs. file store), integrations, and security.

    Viewers will take away techniques they can immediately apply to their own projects.

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