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Big Data Management

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  • Semantic AI: Bringing Machine Learning and Knowledge Graphs Together
    Semantic AI: Bringing Machine Learning and Knowledge Graphs Together Kirk Borne, Principal Data Scientist, Booz Allen Hamilton & Andreas Blumauer, CEO, Managing Partner Semantic Web Company Recorded: May 23 2018 64 mins
    Implementing AI applications based on machine learning is a significant topic for organizations embracing digital transformation. By 2020, 30% of CIOs will include AI in their top five investment priorities according to Gartner’s Top 10 Strategic Technology Trends for 2018: Intelligent Apps and Analytics. But to deliver on the AI promise, organizations need to generate good quality data to train the algorithms. Failure to do so will result in the following scenario: "When you automate a mess, you get an automated mess."

    This webinar covers:

    - An introduction to machine learning use cases and challenges provided by Kirk Borne, Principal Data Scientist at Booz Allen Hamilton and top data science and big data influencer.
    - How to achieve good data quality based on harmonized semantic metadata presented by Andreas Blumauer, CEO and co-founder of Semantic Web Company and a pioneer in the application of semantic web standards for enterprise data integration.
    - How to apply a combined approach when semantic knowledge models and machine learning build the basis of your cognitive computing. (See Attachment: The Knowledge Graph as the Default Data Model for Machine Learning)
    - Why a combination of machine and human computation approaches is required, not only from an ethical but also from a technical perspective.
  • Audit Ex Machina: Digital Learning Systems and Transactional Data
    Audit Ex Machina: Digital Learning Systems and Transactional Data Erik McBain, Strategic Account Manager, MindBridge Ai, Recorded: May 17 2018 44 mins
    How are financial service firms around the world using machine learning systems today to identify and address risk in transactional datasets?

    This webinar will look at a new approach to transaction analysis and illustrate how the combination of traditional rules-based approaches can be augmented with next-generation machine learning systems to uncover more in the data, faster and more efficiently.

    We will span the various applications in banking, payments, trading, and compliance; looking at a variety of use cases from bank branch transaction analysis to trading data validation.

    Anyone interested in financial technology, next-generation machine learning systems and the future of the financial services industry will find this webinar of specific interest.

    About the speaker:
    Erik McBain, CFA is a Strategic Account Manager for MindBridge Ai, where he specializes in the deployment of emerging technologies such as artificial intelligence and machine learning systems in global financial institutions and corporations. Over his 10-year career in banking and financial services(Deutsche Bank, CIBCWM, Central Banking), Erik has been immersed in the trading, analysis, and sale of financial instruments and the deployment of new payment, banking and intelligent technologies. Erik's focus is identifying the various opportunities created through technological disruption, creating partnerships, and applying a client-centered innovation process to create transformative experiences, products, and services for his clients.
  • The Teslification of Banking: The Role of Ethical AI in Sustainable Finance
    The Teslification of Banking: The Role of Ethical AI in Sustainable Finance Richard Peers, Director Financial Services Industry, Microsoft Recorded: May 17 2018 37 mins
    Artificial Intelligence has a huge role to play in banking, no more so than in sustainable finance. However, data is very patchy and much source data is not available to inform Sustainable Finance. The challenge as we set off on this new journey is to make sure that the data and algorithms used are transparent and unbiased.

    In this session, Richard Peers, Director of Financial Services industry at Microsoft will share how disruption and new entrants are bringing new business models and technology to play in banking as in other industries like the Auto Industry

    One new area is sustainable Finance, a voluntary initiative as part of the COP agreement on climate change but the data to inform the markets is a challenge. Big Data, Machine Learning and AI can help resolve this.

    But with such important issues at stake, this session will outline how AI much be designed to ethical principles

    Tune in to this session for a high-level view of some key trends and technologies in banking. Get insight into sustainable finance; why AI can help and why Ethical AI is important; and the Microsoft principles for Ethical AI.
  • Network Telemetry & Analytics in the Age of Big Data & AI
    Network Telemetry & Analytics in the Age of Big Data & AI Ruturaj Pathak, Senior Product Manager, Networking BU, Inventec Recorded: May 15 2018 35 mins
    We are seeing a sea change in networking. SDN has enabled improvements in network telemetry and analytics.

    In this presentation, I will talk about the current challenges that are out there and how the technology change is helping us to improve the overall network telemetry. Furthermore, I will share how deep learning techniques are being used in this field. Please join this webinar to understand how the field of network telemetry is changing.
  • Open Banking - Data, Analytics and the Tragedy of the Commons
    Open Banking - Data, Analytics and the Tragedy of the Commons Dr Louise Beaumont (techUK), Natasha Kyprianides (Hellenic Bank), Tony Fish (AMF Ventures), Katrina Cruz (Anthemis Group) Recorded: May 15 2018 59 mins
    The tragedy of the commons, first described by biologist Garrett Hardin in 1968, describes how shared resources are overused and eventually depleted. He compared shared resources to a common grazing pasture; in this scenario, everyone with rights to the pasture acting in self-interest for the greatest short-term personal gain depletes the resource until it is no longer viable.

    The banking ecosystem and the data that binds it together is not all that different. For many years, through miss-selling scandals, cookie cutter products and dumb mass-marketing have seen players acting in their own interest in accordance to what they believe the ecosystem should look like, how it should evolve and who controls it.

    But with the introduction of open banking, there are signs that new banking ecosystems are set to thrive. Taking Hardin’s notion, collaboration in the open banking future could benefit everyone in the ecosystem – the traditional banks, the FinTechs, the tech titans with their expertise in delivering services at scale, and yet-to-be-defined participants, likely to include the large data players such as energy firms, retailers and telcos.

    Join me to explore the Open Future.
  • Implementing a Sparse Logistic Regression Algorithm in Apache Spark
    Implementing a Sparse Logistic Regression Algorithm in Apache Spark Lorand Dali, Data Scientist, Zalando Recorded: Mar 29 2018 39 mins
    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
    Having fun with Raspberry(s) and Apache Projects Jean-Frederic Clere, Manager, Software Engineering, Red Hat Recorded: Mar 29 2018 49 mins
    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:

    - Raspberry as small server and captive portal using httpd/tomcat.
    - Raspberry as a IoT Sensor collecting data and sending it to ActiveMQ.
    - Raspberry as a Modbus supervisor controlling an Industruino
    (Industrial Arduino) and connected to ActiveMQ.
  • Comparing Apache Ignite & Cassandra for Hybrid Transactional Analytical Apps
    Comparing Apache Ignite & Cassandra for Hybrid Transactional Analytical Apps Denis Magda, Director of Product Management, GridGain Systems Recorded: Mar 28 2018 61 mins
    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
    How to Share State Across Multiple Apache Spark Jobs using Apache Ignite Akmal Chaudhri, Technology Evangelist, GridGain Systems Recorded: Mar 28 2018 42 mins
    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
    Scalable Monitoring for the Growing CERN Infrastructure Daniel Lanza Garcia, Big Data Engineer, CERN Recorded: Mar 28 2018 45 mins
    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.

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