Hi [[ session.user.profile.firstName ]]

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
Your place is confirmed,
we'll send you email reminders
Presented by
Maloy Manna Data engineering PM, AXA Data Innovation Lab
Presentation preview: From Big Data to AI: Building Machine Learning Applications

Network with like-minded attendees

  • [[ session.user.profile.displayName ]]
    Add a photo
    • [[ session.user.profile.displayName ]]
    • [[ session.user.profile.jobTitle ]]
    • [[ session.user.profile.companyName ]]
    • [[ userProfileTemplateHelper.getLocation(session.user.profile) ]]
  • [[ card.displayName ]]
    • [[ card.displayName ]]
    • [[ card.jobTitle ]]
    • [[ card.companyName ]]
    • [[ userProfileTemplateHelper.getLocation(card) ]]
  • Channel
  • Channel profile
  • Architecting an Open Source Data Science Platform: 2018 Edition Oct 23 2018 5:00 pm UTC 60 mins
    David Talby, CTO, Pacific AI
    Join David Talby of Pacific AI as he updates the best practices and techniques for building an open source data science platform.
  • State of the art natural language understanding in healthcare Aug 30 2018 5:00 pm UTC 60 mins
    David Talby, CTO, Pacific AI
    The ability of software to reason, answer questions and intelligently converse about clinical notes, patient stories or biomedical papers has risen dramatically in the past few years.

    This talk for practicing data scientists and software engineers covers state of the art natural language processing, deep learning, and machine learning libraries in this space. We'll share benchmarks from industry & research projects on use cases such as clinical data abstraction, patient risk prediction, named entity recognition & resolution, negation scope detection, and cohort selection.
  • The Teslification of Banking: The Role of Ethical AI in Sustainable Finance May 17 2018 10:00 am UTC 45 mins
    Richard Peers, Director Financial Services Industry, Microsoft
    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.
  • Architecting a Security & Compliance Ready Data Science Platform Recorded: Mar 27 2018 63 mins
    David Talby, CTO, Pacific AI
    Join David Talby of Pacific AI as he overviews the security and compliance checklist when architecting a data science platform.
  • How can Financial Services use AI to reduce unconscious bias? Recorded: Mar 27 2018 6 mins
    Helen Mitchell, FinTech Specialist, Venture Capital Unit, UK Department for International Trade
    Helen Mitchell, FinTech Specialist, Venture Capital Unit, UK Department for International Trade talks about AI and its role within Financial Services.

    How can FS use AI to reduce unconscious bias within their organisations?
    What are the key challenges in adopting AI within FS?
    Will the AI era present a new opportunity for women in FS and FinTech?
  • Neural Networks/Deep Learning to Transform Modern AI Platform Recorded: Feb 22 2018 64 mins
    Dr. Umesh Hodeghatta Rao, CTO, Nu-Sigma Analytics Labs
    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.
  • Panel Discussion: The Road to a Data-Driven Business Recorded: Feb 22 2018 62 mins
    Jen Stirrup, Gordon Tredgold, Joanna Schloss, & Lyndsay Wise
    Join Jenn Stirrup (Director, DataRelish), Gordon Tredgold (CEO & Founder, Leadership Principles LLC), Joanna Schloss (Data Expert) and Lyndsay Wise (Solution Director, Information Builders) as they discuss what it takes to take a business from needing analytics to leveraging analytics successfully.
  • Artificial Intelligence in FinTech: Taking the Leap Recorded: Jan 25 2018 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.

    Moderator:
    Roshan Rohatgi, Senior Innovation & Entrepreneurship Professional, RBS

    Speakers:
    Clare Flynn Levy, Founder and CEO, Essentia Analytics
    Benedetta Arese Lucini, Co-Founder and CEO, Oval Money
    Dom Barker, CTO and Co-Founder, Fluidly
  • 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
    Rob Anderson, Head of Field Operations (Privitar),Tim Hickman, Associate (White & Case)
    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.

    This roundtable will cover a few key questions on data utility and privacy:

    - 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
    This session explores what artificial intelligence can add to data analysis in the social housing sector. Is it better than traditional statistical techniques?

    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
    David Talby, CTO, Pacific AI
    This talk shows how to build a scalable data science platform, using only free, commercially-friendly open source software. The end-to-end architecture covers interactive queries & visualization, machine learning & data mining, deploying models to production, and a full 24x7 operations toolset.

    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.

    This talk is intended for practicing architects and technology leaders, who need to understand how to best leverage the open source ecosystem in this space and what it takes to integrate the available cutting-edge technologies into a cohesive, enterprise-grade and production-grade architecture.
  • Engaging Content Creation with Artificial Intelligence Recorded: Oct 19 2017 39 mins
    Dhaval Bhatt, Founder and CEO of Resonate AI
    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.

    In this webinar, we will cover:
    1) How to identify which tone of voice is generating high engagement for your brand
    2) How to identify the type of story that resonates with your audience?
    3) How to identify the style of voice that generates high-engagement for your brand
  • Image recognition with deep learning Recorded: Oct 11 2017 39 mins
    Layla Tadjpour, Data Science Consultant, Ph.D. in Electrical Engineering from University of Southern California.
    In this webinar, we will learn about image recognition with deep learning. After a brief overview of what deep learning is, and why it matters, we will learn how to classify dogs from cats. That is, how to train a model to recognize dog images from cat images.

    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.
    As business operators how do we get access to these valuable business insights, even when there is not a data analyst around to walk us through their results?

    - Should your software emulate a data scientist?
    - Learn about the power of data visualizations.
    - Learn about creating value from disperse data sets.
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.

Embed in website or blog

Successfully added emails: 0
Remove all
  • Title: From Big Data to AI: Building Machine Learning Applications
  • Live at: Dec 12 2017 12:00 pm
  • Presented by: Maloy Manna Data engineering PM, AXA Data Innovation Lab
  • From:
Your email has been sent.
or close