#datalift No 1 introduction for 25 September 2020 by AI Guild

3744945b353aca873f215d885310cc4c?s=47 Dania Meira
September 21, 2020

#datalift No 1 introduction for 25 September 2020 by AI Guild

AI Guild remote live event on Friday, September 25 at 11 CEST: 90 minutes of groundbreaking insight into deploying data analytics and machine learning at scale with

Peter Heise, Data Analytics and AI Community Leader, Airbus
(Moderated by Chris Armbruster, PhD)

Irina Vidal Migallón, Technical Lead AI & Computer Vision, Siemens Mobility
(Moderated by Sven Krueger)

Aleksandra Bogojeska Kovachev, Senior Machine Learning Engineer, Delivery Hero
(with Varun Chitale and moderated by Macarena Beigier-Bompadre)

Vaibhav Singh, Senior Data Science Manager, Klarna
(Moderated by Corrie Bartelheimer)

Nicholas Hoff, Founder, New Approach Technologies
(Moderated by Evan Simpson)

Evdokia Kazimirova, Machine Learning researcher, xbird GmbH
(Moderated by Marija Vlajic Wheeler)

Alexey Grigorev, Lead Data Scientist, OLX Group
(Moderated by Henrieke Max)

3744945b353aca873f215d885310cc4c?s=128

Dania Meira

September 21, 2020
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Transcript

  1. • What you will get out of today’s event •

    Why we #datalift business and government in Europe • How you can contribute
  2. Collaborate with the leading experts in exploring use cases

  3. Explore some use cases in depth, and get access to

    all content
  4. Make new personal connections with peers

  5. The COVID-19 pandemic is a very difficult moment It is

    impacting our health, economy, and livelihood and it is spurring the creation of digital data Business and government should seize the one great opportunity we have: Scale the data economy
  6. DEPLOY Let's break the proof-of- concept cycle and productionize data

    analytics and machine learning What companies need • More and better expertise for deploying use cases successfully • A structure for management, experts, and talents to ship the product • The will and resources to win big in the data economy
  7. We provide #datalift by tapping the community • 450+ startup

    and industry practitioners in data analytics and machine learning. • 100+ senior leaders on industry use cases, training, consultancy, or freelance services. • Thousands of talents ready for a first data role.
  8. Get ready to show your use case in production at

    one of the next events www.thedatalift.eu
  9. Join the 450+ AI Guild members to make the personal

    connection with peers www.theguild.ai
  10. Work with us on improving #datacareers and on deploying more

    data analytics and machine learning in production
  11. The Airbus AI story - Upskilling 1000s of engineers to

    data analytics and machine learning Dr. Peter Heise Data Analytics and AI Community Leader, Airbus Peter is the Data Analytics and AI Community Leader at Airbus. He holds a Ph.D. in Computer Science; has been at Airbus since 2013; and his mission is building a truly data- driven company serving better products. “It is critical that businesses take an active role in supporting their existing workforce through reskilling and upskilling, and that individuals take a proactive approach to their own lifelong learning. Why? Digitization, automation, and AI already produce a profound transformation in both the type and nature of future jobs and in the structure of the workforce. Learn how Airbus is tackling this issue and at the same time building a data-driven company.” Moderated by Dr. Chris Armbruster
  12. Railway maintenance and autonomous vehicles: more similar and more different

    than you'd think Irina Vidal Migallón Technical Lead AI & Computer Vision, Siemens Mobility Irina is a Senior Computer Vision Engineer and AI Tech Lead at Siemens Mobility. Her background is in MedTech and Computer Vision startups for 10+ years and, since 2017, in Mobility. “Monitoring the state of rail tracks and autonomous driving have many tasks in common. Detect obstacles, detect people, detect assets, detect damages... As pre-production work, it may seem a no-brainer to approach them in similar ways. The devil is in the details, though, which happen to be 90% of the product. Sensors, deployment platform, processing hardware, feedback loop, laws: they have a bigger impact than the machine learning technique itself.” Moderated by Sven Krüger
  13. Marketplace recommendation system. How Delivery Hero discovers where, when, and

    what customers want. Dr. Aleksandra Kovachev Senior Machine Learning Engineer, Delivery Hero Aleksandra is Senior Machine Learning Engineer at Delivery Hero, one of the world’s leading online food ordering and delivery marketplaces. She holds a Ph.D. in Computer Science and Engineering. “The Recommendations and Ranking Team at Delivery Hero is on a mission to discover where, when, and what our customers want. We couple scalable and efficient data pipelines with fast and reliable APIs to serve recommendations for >6 million transactions daily. Join us on the `Personalized customer experience` track to learn how we productionize our ML pipelines.” For the use case presentation, Aleksandra will be joined by her colleague Varun Chitale Moderated by Macarena Beigier-Bompadre
  14. Automated credit risk assessment and fraud detection in online payments.

    How Klarna deploys at scale. Vaibhav Singh Senior Data Science Manager, Klarna Vaibhav is the Senior Data Science Manager at Klarna, Europe's most highly valued fintech startup. He holds an MSc in Communications System Engineering from the University of Portsmouth, UK. “Klarna uses Machine Learning for Credit Risk and Fraud Risk assessments for >1m online shopping transactions daily. Millions of people do online transactions every day and as is evident, the online shopping trend has grown significantly during the Covid-19 pandemic. Gone are the days when we could evaluate all transactions manually. To be a market leader and better than the competition, automated decisions need to be made which should be accurate and fast. We will show you how the leading payment solutions company Klarna uses Machine Learning to do Credit Risk and Fraud Risk assessments at scale. You will get a glimpse of how ML-based decision-making is done and what data engineering needs to be in place to serve the online models.” Moderated by Corrie Bartelheimer
  15. Automated decision making for supplier management. Invoice classification for Société

    Générale. Dr. Nicholas Hoff Founder, New Approach Technologies Nick is a Machine Learning expert for freelance projects and the founder of New Approach Technologies. He holds an MSc in Aerospace Engineering from MIT, and a Harvard Ph.D. in AI. “Can you automatically, and accurately, decide whether to pay an invoice? How to automate decision making at the confidence level 99.5% or higher, and have it vetted by experts. One of our clients (Société Générale) operates a corporate car leasing program in one of its subsidiaries. Since the cars belong to the bank, the bank is responsible for paying the maintenance and repair invoices. Repair shops send tens of thousands of invoices directly to the bank and experienced people examine the invoices individually to decide whether they should be paid. Most invoices are paid, but some are rejected for errors, fraud, or because they violate a pre-existing contract between the bank and the particular maintenance shop. Our task is to train a classification engine on several years of historical invoices so that it can learn how the people make their decisions, then use this engine to automatically decide whether to pay future invoices without sacrificing accuracy.” Moderated by Evan Simpson
  16. Moving towards personalized digital healthcare at xbird for people living

    with diabetes Evdokia Kazimirova Machine Learning researcher, xbird Eva is an ML Researcher working on blood glucose prediction and activity recognition. She holds a PhD in Physiology from the Russian Academy of Science and has 6+ years experience in the field. “How xbird classifies daily activity data to improve personalized therapy for people living with diabetes. The process from data collection to pipeline choice. Diabetes is a complicated chronic disease, and the progression and impact on each person depend on a variety of factors. The disease should thus be treated according to the individual lifestyle. Essential is the tracking and processing of huge amounts of daily events and understanding their causations and correlations. Patients need support based not only on their medical data like blood glucose level, insulin intake, etc. but also on tracking the daily activities as the main influencing factor.” Moderated by Marija Vlajic Wheeler
  17. Image metadata extraction at scale for an online classifieds’ platform.

    How OLX productionizes Deep Learning models. Alexey Grigorev Lead Data Scientist, OLX Alexey is the Lead Data Scientist at OLX Group, a global marketplace for online classified advertisements. Alexey is also a Kaggle master and author of the book Machine Learning Bookcamp. “How OLX applies deep learning models to 10 million images per day to automatically support sellers in their platform to attract buyers by: using the right cover image for the listing, and good quality images. Images contain a lot of important information and being able to use this information is often crucial for an internet company — especially as on OLX 10 million new images are created every day. Such an image metadata service can also be used for content moderation (detect and alert forbidden or illegal items on sale), and for image deduplication.” Moderated by Henrieke Max
  18. On the AI Guild team effort for #datalift No 1

    on 25 September 2020 The #datalift campaign and events are the result of a joint effort by the members of the AI Guild. Since the founding in May 2019, the AI Guild community has grown to 450+ members, all active in the field of data analytics and machine learning in tech, product, and business roles. The #datalift campaign website was co-developed by a task force of a dozen members. Likewise, #datalift No 1 on 25 September was co-developed by the AI Guild community, with the core of the use cases coming from members, and the presenters and moderators volunteering to support companies and governments in Europe in learning better sand faster how to productionize data analytics and machine learning. If you would like to join this community, please visit theguild.ai For the AI Guild, Dania Meira and Chris Armbruster