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Be Proactive With Ops Analytics - Leverage your Splunk Investment

Learn to predict, prepare for and pinpoint performance issues by combining HP OpsA and J9 Accelerators with Splunk. This webinar will cover how your IT operations teams will be able to extract the full value of their data.

If you’ve made an investment in Splunk and want to increase functionality, while at the same time reducing maintenance costs, you need to attend.
Recorded Oct 8 2015 67 mins
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Presented by
Clay Roach, J9 Technologies
Presentation preview: Be Proactive With Ops Analytics - Leverage your Splunk Investment

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  • How to Achieve Cloud Control Using Micro Focus Hybrid Cloud Management Recorded: Apr 18 2018 58 mins
    Shaw Zhang, Neil Miles
    Brought to you by the Vivit Automation & Cloud Builders Special Interest (SIG) Group.

    Attend this webinar to receive a live demonstration of public cloud service brokering, aggregation and governance using the Micro Focus Hybrid Cloud Management solution. HCM allows IT to easily aggregate public cloud resources into an end-user catalog, while providing governance and analytics to ensure business unit consumption is visible and within budget. In addition, learn how improved control of public cloud spending can help you start building a strong business case for cloud management.

    Cloud Aggregation & Brokering
    - Cloud service aggregation to quickly aggregate hybrid cloud services and publish offerings in catalog
    - Self-service web portal access to catalog services for end users to consume services

    Hybrid Cloud Governance
    - Get visibility into IT cloud costs with show-back reporting for LOBs/organizations
    - Policy-based budget quota management to proactively track cloud costs and notify on quota overages
  • [Ep.9] Founders Spotlight : Eva Murray & Andy Kriebel of Makeover Monday Recorded: Apr 3 2018 26 mins
    Andy Kriebel, Head Coach and Tableau Zen Master at The Data School & Eva Murray, Head of BI and Tableau Zen Master at Exasol
    This webinar is part of BrightTALK's Founders Spotlight series, featuring fearless entrepreneurs and inspiring founders.

    In this episode, Eva Murray & Andy Kriebel, Founders of Makeover Monday, will share their story of how they started the social data project, Makeover Monday, the challenges and successes they encountered along the way and how they overcame them.

    This will be an interactive Q&A session and an excellent opportunity for entrepreneurs or professionals to have their questions answered.
  • Implementing a Sparse Logistic Regression Algorithm in Apache Spark Recorded: Mar 29 2018 39 mins
    Lorand Dali, Data Scientist, Zalando
    This talk tells the story of implementation and optimization of a sparse logistic regression algorithm in spark. I would like to share the lessons I learned and the steps I had to take to improve the speed of execution and convergence of my initial naive implementation. The message isn’t to convince the audience that logistic regression is great and my implementation is awesome, rather it will give details about how it works under the hood, and general tips for implementing an iterative parallel machine learning algorithm in spark.

    The talk is structured as a sequence of “lessons learned” that are shown in form of code examples building on the initial naive implementation. The performance impact of each “lesson” on execution time and speed of convergence is measured on benchmark datasets.

    You will see how to formulate logistic regression in a parallel setting, how to avoid data shuffles, when to use a custom partitioner, how to use the ‘aggregate’ and ‘treeAggregate’ functions, how momentum can accelerate the convergence of gradient descent, and much more. I will assume basic understanding of machine learning and some prior knowledge of spark. The code examples are written in scala, and the code will be made available for each step in the walkthrough.

    Lorand is a data scientist working on risk management and fraud prevention for the payment processing system of Zalando, the leading fashion platform in Europe. Previously, Lorand has developed highly scalable low-latency machine learning algorithms for real-time bidding in online advertising.
  • Having fun with Raspberry(s) and Apache Projects Recorded: Mar 29 2018 49 mins
    Jean-Frederic Clere, Manager, Software Engineering, Red Hat
    You can do a lot with a Raspberry and ASF projects. From a tiny object
    connected to the internet to a small server application. The presentation
    will explain and demo the following:

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    - Raspberry as a Modbus supervisor controlling an Industruino
    (Industrial Arduino) and connected to ActiveMQ.
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    The 10x growth of transaction volumes, 50x growth in data volumes and drive for real-time visibility and responsiveness over the last decade have pushed traditional technologies including databases beyond their limits. Your choices are either buy expensive hardware to accelerate the wrong architecture, or do what other companies have started to do and invest in technologies being used for modern hybrid transactional analytical applications (HTAP).

    Learn some of the current best practices in building HTAP applications, and the differences between two of the more common technologies companies use: Apache® Cassandra™ and Apache® Ignite™. This session will cover:

    - The requirements for real-time, high volume HTAP applications
    - Architectural best practices, including how in-memory computing fits in and has eliminated tradeoffs between consistency, speed and scale
    - A detailed comparison of Apache Ignite and GridGain® for HTAP applications

    About the speaker: Denis Magda is the Director of Product Management at GridGain Systems, and Vice President of the Apache Ignite PMC. He is an expert in distributed systems and platforms who actively contributes to Apache Ignite and helps companies and individuals deploy it for mission-critical applications. You can be sure to come across Denis at conferences, workshop and other events sharing his knowledge about use case, best practices, and implementation tips and tricks on how to build efficient applications with in-memory data grids, distributed databases and in-memory computing platforms including Apache Ignite and GridGain.

    Before joining GridGain and becoming a part of Apache Ignite community, Denis worked for Oracle where he led the Java ME Embedded Porting Team -- helping bring Java to IoT.
  • How to Share State Across Multiple Apache Spark Jobs using Apache Ignite Recorded: Mar 28 2018 42 mins
    Akmal Chaudhri, Technology Evangelist, GridGain Systems
    Attend this session to learn how to easily share state in-memory across multiple Spark jobs, either within the same application or between different Spark applications using an implementation of the Spark RDD abstraction provided in Apache Ignite. During the talk, attendees will learn in detail how IgniteRDD – an implementation of native Spark RDD and DataFrame APIs – shares the state of the RDD across other Spark jobs, applications and workers. Examples will show how IgniteRDD, with its advanced in-memory indexing capabilities, allows execution of SQL queries many times faster than native Spark RDDs or Data Frames.

    Akmal Chaudhri has over 25 years experience in IT and has previously held roles as a developer, consultant, product strategist and technical trainer. He has worked for several blue-chip companies such as Reuters and IBM, and also the Big Data startups Hortonworks (Hadoop) and DataStax (Cassandra NoSQL Database). He holds a BSc (1st Class Hons.) in Computing and Information Systems, MSc in Business Systems Analysis and Design and a PhD in Computer Science. He is a Member of the British Computer Society (MBCS) and a Chartered IT Professional (CITP).
  • Scalable Monitoring for the Growing CERN Infrastructure Recorded: Mar 28 2018 45 mins
    Daniel Lanza Garcia, Big Data Engineer, CERN
    When monitoring an increasing number of machines, the infrastructure and tools need to be rethinked. A new tool, ExDeMon, for detecting anomalies and raising actions, has been developed to perform well on this growing infrastructure. Considerations of the development and implementation will be shared.

    Daniel has been working at CERN for more than 3 years as Big Data developer, he has been implementing different tools for monitoring the computing infrastructure in the organisation.
  • The Data Lake for Agile Ingest, Discovery, & Analytics in Big Data Environments Recorded: Mar 27 2018 58 mins
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    As data analytics becomes more embedded within organizations, as an enterprise business practice, the methods and principles of agile processes must also be employed.

    Agile includes DataOps, which refers to the tight coupling of data science model-building and model deployment. Agile can also refer to the rapid integration of new data sets into your big data environment for "zero-day" discovery, insights, and actionable intelligence.

    The Data Lake is an advantageous approach to implementing an agile data environment, primarily because of its focus on "schema-on-read", thereby skipping the laborious, time-consuming, and fragile process of database modeling, refactoring, and re-indexing every time a new data set is ingested.

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    The hype surrounding AI has reached a fever pitch, but practical uses that are delivering concrete, practical business results are here, and it’s time to get real.

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    * How to identify the right AI use case for your business
    * Common challenges firms face when implementing AI
    * Why AI’s time in the sun has finally come, and why it’s here to stay

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    Companies aren’t just using these AI tools to enable existing marketing strategies; they’re taking those strategies to the next level, going beyond simple retention and delivering active engagement. AI technology offers companies unprecedented insight into customer behaviors, patterns, and beliefs, allowing you to seamlessly anticipate customer needs and serve up hyper-personalized, emotionally resonant campaigns where and when they’re most welcome.

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    Speakers:
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    * Michael Healey, President, Yeoman Technology Group
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    Sponsored by Lytics
  • Customer Support through Natural Language Processing and Machine Learning Recorded: Feb 22 2018 60 mins
    Robin Marcenac, Sr. Managing Consultant, IBM, Ross Ackerman, Dir. Digital Support Strategy, NetApp, Alex McDonald, SNIA CSI
    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
  • Neural Networks/Deep Learning to Transform Modern AI Platform Recorded: Feb 22 2018 64 mins
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    AI is changing the way organizations do businesses and how they interact with customers. AI continues to drive the change. Deep Learning and Natural Language Processing will become standards in AI solutions. Deep Learning is based on brain simulations and uses deep neural networks. AlphaGo is the first AI system to defeat a professional human Go player, the first program to defeat a Go world champion, and arguably the strongest Go player in history. Baidu improved speech recognition from 89% to 99% using Deep Learning. Every AI and Machine learning scientist is required to know Deep Learning tools in his / her current job scenario.

    In this session, we will be discussing what is Deep Learning and why it is gaining popularity. We will explain AI solutions using Deep Learning with a practical example. Deep Learning has an edge over other machine learning techniques as with the increased volume of data, performance increases with Deep Learning. Further, Deep Learning enables Hierarchical Feature Learning i.e. learning feature hierarchies.
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    Anil Saini, Data Viz Expert, Srijan Technologies
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    Additionally, we'll deeply look into how Embedded Analytics is different from Traditional Business Intelligence and what are the factors/trends driving Embedded Analytics.
  • What is your Land Value, Thessaloniki? A Real (estate) Data Visualization Story Recorded: Feb 20 2018 46 mins
    Charalampos Xanthopoulakis, Data Visualizations Architect
    Selling your house in the financial crisis-stricken Greece is up to this day a great ordeal. When faced with such a challenge, I was baffled by the sparsity of conclusive data on land value at my birthplace city, Thessaloniki. Embarking on a personal mission and collecting and processing more than 10K online housing ads together with open data, I managed to render an insightful interactive visualization of the actual real estate values on borough and city block level that was published through the Greek media. Join me on this thought process journey to find out how to

    o Gather vast online data with simple scripting

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    This will be an interactive session, so please feel free to bring your thoughts and questions to share during the session.
  • RIDE Containerized Data Science IDE server For Enterprise Recorded: Dec 14 2017 45 mins
    Ali Marami, Data Science Advisor at R-Brain
    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.

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    ⁃ Translating 2D into 3D
    ⁃ Interacting with data
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    • Capturing data from virtual environments
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    ⁃ How companies are using captured XR Data
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    We will talk through the role of Apache Arrow, the in-memory columnar data standard that is accelerating analytics for GPU-based processing, as well as the role of Pandas and Arrow in providing unprecedented speed in accessing datasets from Python.
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  • Presented by: Clay Roach, J9 Technologies
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