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From Big Data to AI: Building Machine Learning Applications

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.
Recorded Dec 12 2017 49 mins
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Presented by
Maloy Manna Data engineering PM, AXA Data Innovation Lab
Presentation preview: From Big Data to AI: Building Machine Learning Applications

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  • Architecting an Open Source Data Science Platform: 2018 Edition Oct 23 2018 5:00 pm UTC 60 mins
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    Join David Talby of Pacific AI as he updates the best practices and techniques for building an open source data science platform.
  • Building Reusable Data Assets for Data Science Teams Aug 30 2018 5:00 pm UTC 60 mins
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    Join David Talby of Pacific AI as he overviews Building Reusable Data Assets for Data Science Teams.
  • Architecting a Security, Privacy & Compliance Ready Data Science Platform Mar 27 2018 5:00 pm UTC 60 mins
    David Talby, CTO, Pacific AI
    Join David Talby of Pacific AI as he overviews the security, privacy, and compliance checklist when architecting a data science platform.
  • AI in FinTech: Taking the Leap Jan 25 2018 2:00 pm UTC 60 mins
    Innovate Finance
    Artificial intelligence and machine learning has the potential to transform Financial Services, but so far we have seen few successful use cases. Is 2018 finally the year this technology turns from hype to reality? Which FinTechs are utilising AI best to serve the end consumers and what are the challenges that come with this? Can incumbents see past chatbots and start implementing intelligence products and solution that will make a real impact on their business model? As part of its IFGS 2018 Webinar Series, Innovate Finance will host a discussion looking at the current and potential future impact of AI technology in Financial Services and hear from companies leading the way.
  • 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.

    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.
  • Big Data Analytics vs Privacy: Risks and Opportunities Recorded: Dec 14 2017 58 mins
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    Today's modern businesses gain competitive edge and remain innovative by using advanced analytics and machine learning. Utilising big data can build customer loyalty by improving personalised marketing campaigns; optimises fraud detection; and improves products and services by advanced testing. However, the data sets required for advanced analytics are often sensitive, containing personal customer information, and therefore come with an inherent set of privacy risks and concerns.

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    - In what ways advanced analytics help businesses gain competitive edge?

    - What is defined as sensitive data?

    - Will GDPR affect the way you're allowed to use customer data?

    - What opportunities are there to utilise sensitive data?

    Unlocking the data’s true value is a challenge, but there are a range of tools and techniques that can help. This live discussion will focus on the data analytics landscape; compliance considerations and opportunities for improving data utility in 2018 and beyond.

    Key takeaways:

    - A view of the data protection landscape

    - How to remaining compliant with GDPR when using customer data

    - Use cases for advanced analytics and machine learning

    - Opportunities for maximising data utility in 2018
  • AI in VR: Enabling a world of “6 DoF” Analytics Recorded: Dec 13 2017 42 mins
    John Cutter, Product Manager, Watson Developers Labs & AR/VR Labs, IBM
    Augmented Reality and Virtual Reality hold the potential to affect nearly every facet of daily business. Those implications go beyond the disruptive nature of even the mobile phone.

    Explore the business implications of AR & VR with John as he discusses trends through the lens Big Data, Analytics, and AI.

    John, IBM’s Head of AR/VR Labs, will discuss real-world examples of compelling work in these categories, draw on the lessons learned, and expand on future trends that businesses should seriously consider when using AR or VR in their company.
  • Natural Language Processing Exposed: The Art, the Science and the Applications Recorded: Dec 12 2017 62 mins
    Sid J Reddy, Chief Scientist, Conversica
    In this presentation, we will discuss several applications of NLP such as information extraction, knowledge synthesis, and entity retrieval. We will discuss how these fundamental set of algorithms are applicable for a wide array of use-cases and industry verticals such as healthcare, business intelligence, life sciences, legal, e-commerce, sales, and marketing.

    Additionally, case studies from these areas will be used to provide an intuitive explanation of complex NLP topics such as distributional semantics, computational linguistics, question-answering, conversational AI, and applications of deep learning to text data.

    Dr. Sid J. Reddy's Bio: https://www.conversica.com/sid-j-reddy/
  • Building a Fast, Scalable & Accurate NLP Pipeline on Apache Spark Recorded: Dec 12 2017 62 mins
    David Talby, CTO, Pacific AI
    Natural language processing is a key component in many data science systems that must understand or reason about text. Common use cases include question answering, paraphrasing or summarization, sentiment analysis, natural language BI, language modeling, and disambiguation. Building such systems usually requires combining three types of software libraries: NLP annotation frameworks, machine learning frameworks, and deep learning frameworks.

    This talk introduces the NLP library for Apache Spark. It natively extends the Spark ML pipeline API's which enabling zero-copy, distributed, combined NLP & ML pipelines, which leverage all of Spark's built-in optimizations.

    The library implements core NLP algorithms including lemmatization, part of speech tagging, dependency parsing, named entity recognition, spell checking and sentiment detection. The talk will demonstrate using these algorithms to build commonly used pipelines, using PySpark on notebooks that will be made publicly available after the talk.

    David Talby has over a decade of experience building real-world machine learning, data mining, and NLP systems. He’s a member of the core team that built and open sourced the Spark NLP library.
  • The Human Role in AI Recorded: Dec 12 2017 36 mins
    Peter Bruce, President and Founder, The Institute for Statistics Education at Statistics.com
    Artificial Intelligence (AI) is a hot topic, and there is widespread alarm that AI will replace humans in the analytical process. Adam Selipsky, the CEO of Tableau, terms this a myth, and said recently that AI's role will remain that of an assistant to the analytics professional.

    In this talk we go beyond that, and look at some interesting aspects of the human role as an integral component of machine learning and statistical modeling.

    We discuss how human expertise "supervises" machine learning, how reliance on multiple sources can deliver surprising expertise, and when that system can go wrong.
  • AI in social housing Recorded: Dec 12 2017 18 mins
    Vicki Howe, Head of Product Development, HouseMark; Jason Lee, CEO, illumr
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    HouseMark is the leading provider of data analysis and insight solutions to social housing providers. Illumr took part in HouseMark’s accelerator programme introducing innovative technology start-ups to the social housing sector. HouseMark have worked with illumr to test out the appetite for AI and explore what insights can be gained using data typically held by social housing providers.
  • From Big Data to AI: Building Machine Learning Applications Recorded: Dec 12 2017 49 mins
    Maloy Manna Data engineering PM, AXA Data Innovation Lab
    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.
  • How to Build an Open Sourced Data Science Platform Recorded: Oct 26 2017 75 mins
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    Requirements include what an enterprise typically requires: Strong security (authentication, authorization, audit, encryption, multi-tenancy), active monitoring for both systems & data, backup & restore, user management (with LDAP integration), distributed deployment on commodity hardware, auto scaling, and self-healing when containers or services go down. Technologies covered include Spark, Hadoop, ElasticSearch, Kibana, Jupyter notebooks, TensorFlow, OpenScoring, Docker Swarm, and supporting tools.

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  • Engaging Content Creation with Artificial Intelligence Recorded: Oct 19 2017 39 mins
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    Content marketers struggle with creating engaging content. With social media automation tools, most of the hard-lifting on content distribution is solved. The hard thing about content marketing is not marketing, it's the creation. The age-old question still stands true: How to tell an engaging story that creates "know-like-trust" factor with your audience and results in engagement and sales.

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    3) How to identify the style of voice that generates high-engagement for your brand
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    Layla Tadjpour, Data Science Consultant, Ph.D. in Electrical Engineering from University of Southern California.
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    We use Keras, an easy to use python deep learning library that sits on top of Tensorflow, and “fine-tuning”, a very important skill for any deep learning practitioner, to train a model to classify the images.

    Once we trained our model to classify dogs from cats images with high accuracy, we dig into the details of the trained model and look at its building blocks, i.e., Convolutional Neural Networks (CNN), Fully Connected Block and activation functions to develop an understanding of how the deep learning model works.
  • Data scientists: Can't live with them, can't live without them. Recorded: Aug 24 2017 45 mins
    Wyatt Benno, CEO, DataHero
    There has been a flood of publicity around big data, data processing, and the role of predictive analytics in businesses of the future.
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    - Should your software emulate a data scientist?
    - Learn about the power of data visualizations.
    - Learn about creating value from disperse data sets.
  • Artificial Intelligence: Methods, Applications and Impacts Recorded: Aug 23 2017 49 mins
    Arinze Akutekwe, PhD Data Scientist, BAS EMEIA – Intelligent Enterprise - Analytics at Fujitsu
    Artificial intelligence has greatly changed the way we live since the 20th century. It involves the science and engineering of making machines intelligent and autonomous using computer programs.

    The processing power of computers has been on the exponential increase with cost of processors and storage decreasing. This has made research and developments efforts in AI areas such as deep learning, once thought to be impossible possible.

    In this webinar, we will examine current methods, application domains of specific methods, their impacts on our daily lives and try to answer questions on ethics of applying these technologies.
  • Hunting Criminals with Hybrid Analytics, Semi-supervised Learning, & Feedback Recorded: Aug 23 2017 62 mins
    David Talby, CTO, Pacific AI
    Fraud detection is a classic adversarial analytics challenge: As soon as an automated system successfully learns to stop one scheme, fraudsters move on to attack another way. Each scheme requires looking for different signals (i.e. features) to catch; is relatively rare (one in millions for finance or e-commerce); and may take months to investigate a single case (in healthcare or tax, for example) – making quality training data scarce.

    This talk will cover a code walk-through, the key lessons learned while building such real-world software systems over the past few years. We'll look for fraud signals in public email datasets, using IPython and popular open-source libraries (scikit-learn, statsmodel, nltk, etc.) for data science and Apache Spark as the compute engine for scalable parallel processing.

    David will iteratively build a machine-learned hybrid model – combining features from different data sources and algorithmic approaches, to catch diverse aspects of suspect behavior:

    - Natural language processing: finding keywords in relevant context within unstructured text
    - Statistical NLP: sentiment analysis via supervised machine learning
    - Time series analysis: understanding daily/weekly cycles and changes in habitual behavior
    - Graph analysis: finding actions outside the usual or expected network of people
    - Heuristic rules: finding suspect actions based on past schemes or external datasets
    - Topic modeling: highlighting use of keywords outside an expected context
    - Anomaly detection: Fully unsupervised ranking of unusual behavior

    Apache Spark is used to run these models at scale – in batch mode for model training and with Spark Streaming for production use. We’ll discuss the data model, computation, and feedback workflows, as well as some tools and libraries built on top of the open-source components to enable faster experimentation, optimization, and productization of the models.
  • Using Predictive Analytics to Empower Customer Experience Recorded: Aug 23 2017 44 mins
    Patrick Rice, CEO, Lumidatum
    Predictive analytics & artificial intelligence are transforming the way all companies connect with customers. The customer experience is on the verge of being completely redefined with A.I.

    In this talk, you will learn what it takes for a business to deliver this new 21st-century customer conversation. Patrick will cover:

    - Collecting & Managing CX data - What I learned analyzing 3 billion records at Amazon
    - We have the data, now what? AI & the Future of Customer Experience
    - How to use Predictive Analytics to find the right customer on the right channel
    - 3 ways to drive ROI for your business by applying AI to improve the Customer Experience

    Patrick is CEO of Lumidatum which helps CMOs apply artificial intelligence to the customer experience in order to grow revenue. He has been in the data and analytics space for over a decade including running a machine learning and advanced analytics team at Amazon.
  • Putting AI into LeAdershIp Recorded: Aug 23 2017 45 mins
    Prof. Dr. Michael Feindt, Founder & Chief Scientific Officer, Blue Yonder
    Artificial Intelligence (AI) is not a technology for the future; it’s a huge business opportunity for today. But how can your organisation become a trailblazer for AI innovation, transforming the way you work to deliver immediate – and lasting – bottom line value?

    Former CERN scientist, Prof. Dr. Michael Feindt, is one of the brightest minds in Machine Learning. Join him for a 30-minute masterclass in how to apply AI to your business.

    You’ll learn how AI can:
    •Make sense of market and customer complexity, to deliver quick and effective decisions every single day
    •Increase workforce productivity to improve output and staff morale
    •Enhance decision-making and forecasting accuracy, for operational efficiency and improved productivity
    •Be implemented into your business quickly, easily, with minimal disruption

    Michael will also share real-life examples of how international businesses are using AI as a transformation tool, from his experience as founder of market-leading AI solution provider, Blue Yonder.
A journey of ideas and action from man to machine
This channel covers the advent of artificial intelligence in business and society. Join the discussion with webinars and videos covering everything from neural networks, to computer vision and NLP, to machine learning and AI application in the real world.

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  • Live at: Dec 12 2017 12:00 pm
  • Presented by: Maloy Manna Data engineering PM, AXA Data Innovation Lab
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