• AI IN MARKETING VIRTUAL SUMMIT

  • 09:00

    WELCOME & OPENING REMARKS

  • 09:10
    Javier Perez

    The Growth of AI Open-Source Software in Unexpected Platforms

    Javier Perez - Open Source Programe Strategist - IBM

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    The Growth of AI Open-Source Software in Unexpected Platforms

    Today Open Source Software (OSS) is more prevalent than in any other era and continues to grow with the latest technologies from AI and Data Science to Blockchain and Autonomous Vehicles. In this session, we are going to review AI open-source in unexpected platforms. Specifically, we are going to cover OSS in the modern mainframe, the platform used by most financial services organizations, including now fintech startups every large financial institution.

    Tensorflow, Python, Spark, and many other widely used OSS have become the building blocks of all AI and ML applications. Open-source is addressing the major trends in the Financial industry: Modernization with AI and big data, regulatory compliance, and DevOps.
    Open Source Software for mainframes is neither widely known nor something new. This session is going to present information on how open source is done for mainframes and how to port existing software to a modern platform available in all Linux distributions.

    Takeaways: • Learn about available open source software in AI • Learn about the platform of choice for AI in Financial institutions • Learn how to continue the growth of the open-source ecosystem for AI

    Bio: Javier Perez leads the Open Source Program for the IBM Z and LinuxONE ecosystem at IBM. Javier has been in the Open Source, Cloud, SaaS, and Mobile industries for 20+ years. He has been working directly with Open Source Software (OSS) for over 10 years, more recently leading product strategy of the Software Composition Analysis product line at Veracode. Prior to Veracode, Javier was at Axway leading a successful open source project, Appcelerator, and at Red Hat where he was Director of Product Management driving the OpenShift-based Mobile Application Platform offering for developers and enterprises including containerized applications. Javier has had the opportunity to speak at webinars and conferences all over the world covering open source, security, cloud, and application development topics. Javier has held leadership positions in Product Management and Sales Engineering for different startups, leading successful product exits and product integrations post-acquisition. Javier holds an honors degree in Computer Systems and an MBA.

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  • PLENARY SESSION

  • THE CURRENT AI LANDSCAPE

  • 09:30

    Implementing AI in Retail & Marketing

  • AI TOOLS FOR RETAIL & MARKETING

  • 09:50

    Where Deep Learning Fails

  • 10:10

    Taking Machine Learning to Production

  • CUSTOMER EXPERIENCE

  • 10:30

    Providing a Better Experience to Your Audiences

  • 10:50

    COFFEE

  • 11:30

    AI Copy Writers to Build Efficiency in Ad Generation

  • APPLICATIONS IN RETAIL

  • 11:50

    Using Computer Vision to Predict Fashion Trends

  • 12:10
    Michael Sollami

    AI for E-Commerce

    Michael Sollami - Lead Data Scientist - Salesforce

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    Building Visual Search at Salesforce

    Fine-grain recognition remains an unsolved problem at in the general case, indeed, it may even be as difficult as self-driving cars. There are many technical challenges in achieving accurate production-level image retrieval at web scale (handling catalogs of tens of millions of items). This talk details the steps and highlights the hurdles in building such a search platform. At Commerce Cloud Einstein, we have developed a custom multi-stage pipeline of deep metric learning models for product detection and recognition. Our networks are trained to discover a manifold representing the space of all consumer products. We will present the current architectures in our embedding networks, i.e. the mapping from consumer images to the product feature space, as well as the most promising research directions. Implementation level details will be covered insofar as they make efficient fine-grain retrieval possible, and performance evaluation (both statistical as well as qualitative) measures will be described.

    Michael received a doctorate in mathematics from the University of Wyoming. Since 2012 he has led research and development teams at a number of successful Boston-based startups. Currently a lead data scientist on Salesforce's Einstein team, he enoys designing and building deep learning systems with applications to e-commerce and computer vision.

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  • 12:30

    Online Supermarkets- From Convenient to Smart Shopping

  • 12:50

    Networking & Discussion

  • 09:00

    Welcome & Opening Remarks

  • SEARCH & RECOMMENDATION

  • 09:10

    Music Recommendations

  • 09:30

    Personalising Product Recommendations

  • 09:50
    Bettina Hermant

    Developing a Recommender System for a Public Service Broadcaster

    Bettina Hermant - Senior Data Scientist - BBC

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    Developing a Recommender System for a Public Service Organisation

    The BBC is on a journey to make our audience experience more relevant and personalised, and a key part of our success lies in our ability to provide recommendations. At the BBC, we believe that recommendations should reflect the breadth and diversity of our content, while meeting our editorial guidelines. In this talk, I will describe how we develop a recommender system for BBC Sounds, including the Machine Learning model used, the architecture behind the engine, and the unique challenges we face to uphold the editorial policy and the values of the organisation.

    Bettina is a Senior Data Scientist at the BBC. Her team aims to use machine learning algorithms to provide a better experience to their audiences, mainly through personalisation. She works very closely with the Data Engineering, Editorial and Product teams. Bettina has mostly been involved in building a recommender system for production use for one of the main BBC products. The Machine Learning algorithm used is hybrid and the code is developed in Python. Google Cloud Platform tools are used to manage the resources and to store the data, Airflow for the automation, and Redis for serving. Bettina has been involved in all of the steps: from the algorithm development, to the engine productionisation, but also in making sure that the recommendations are compliant with the editorial policies and company values.

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  • AI IN ADVERTISING

  • 10:10

    Speech Recognition and the Future of Consumerism

  • 10:30

    COFFEE

  • PREDICTIONS & FORECASTS

  • 11:15

    Implementation of Churn prediction and its Efficiency Estimation

  • 11:35
    Sofie De Cnudde

    Predicting Purchases using Deep Learning

    Sofie De Cnudde - Machine Learning Scientist - ASOS

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    Personalising Product Recommendations at ASOS

    ASOS' recommendations are loved by its customers and are one of ASOS' core AI projects. One application of our recommender system is the You Might Also Like feature on product pages which shows similar products to customers. This talk will focus on the journey we embarked on where we aimed at personalising these product recommendations for our customers. We will talk about how we built hypotheses, how we iterated over multiple (simple and complex) models, how we performed offline and onvline tests, how we collaborated with engineers and most importantly about the successes and failures along this journey.

    Sofie De Cnudde is a Machine Learning Scientist at ASOS.com. After obtaining Master’s degrees in Computer Science and Business Economics at Ghent University, she started a PhD at the University of Antwerp. Her PhD was focused on how to leverage fine-grained, human behavioural data to make predictions about people’s future actions or interests. Four publications resulted from the research where theoretical results were applied to benefit areas such as micro lending, cultural government programs and retail. She started working at ASOS in 2018 and has worked across different business areas such as Supply Chain Optimisation and Marketing, and is currently working in Recommendations.

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  • 11:55

    PANEL: How to Avoid Getting Left Behind in a Data-Driven Future

  • 12:30

    Networking & Discussion

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