#datalift No 4 by AI Guild - Introduction for 26 March 2021

AI Guild free live event on Friday, March 26 at 11.00 CET.

Experts introduce use cases for data analytics and machine learning in AI startups, automotive, manufacturing, marketplaces, and responsible AI.

Welcome to the new venue that we hope offers you an enhanced experience by connecting to all other participants in a seamless flow of main stage interview, use case session, networking, and selected partners.

Petabyte-scale development of autonomous driving
Interview with Dr. Boris Schauerte, Data Science and Engineering Leader at BMW. Hear how Boris has been pushing BMW's data-driven development processes, tools, and backend for autonomous driving.
Moderated by Dr. Chris Armbruster

Increasing the value and impact of AI for business
Interview with Nicole Büttner, Co-Founder & CEO Merantix Labs, and Digital Leader at World Economic Forum. Hear from Nicole which approaches work for creating value for industry clients.
Moderated by Dânia Meira

ML-based object recognition for autonomous driving
Julia Nitsch, Machine Learning Engineer, Ibeo Automotive
Moderated by Andrés Prada González

ML for factory production excellence. Using machine data to optimize
Dr. Edith Chorev, Head of Data Science, FactoryPal
Moderated by Irem Nasir

Recommendation engines for marketplaces. ML for Personalised user experience
Dr. Yernat Assylbekov, Data Scientist, Delivery Hero
Moderated by Aline Quadros

Privacy-preserving ML with Differential Privacy in healthcare
Andreas Kopp, Digital Advisor and AI Practitioner, Microsoft
Moderated by Fabian Harder

Transcript

1. Welcome! Get the #datalift e-book for free today: bit.ly/datalift-ebook #datalift No 4 26 March 2021
2. • What you will get out of today’s event • Why we #datalift business and government in Europe • How you can contribute Deploy data analytics and machine learning #datalift No 4 26 March 2021
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. We provide #datalift by tapping the community • 600+ startup and industry practitioners in data analytics and machine learning. • 150+ senior leaders on industry use cases, training, consultancy, or freelance services. • Thousands of talents ready for a first data role.
7. DEPLOY Let's break the proof-of-concept cycle and productionize data analytics and machine learning Benchmark for your industry, and get a second opinion on your proof-of-concepts Optimize architecture and pipelines for production Lead your industry with best practices for continuous deployment www.thedatalift.eu/services
8. Ecosystem partnership #datalift Become a partner in building the ecosystem for productionizing data analytics and machine learning thedatalift.eu/partnership
9. The #datalift e-book is available Get it for free today* 60 pages with insights from the 2 interviews and 10 use cases in production presented by AI Guild members during #datalift No 1 & No 2 Click to download: bit.ly/datalift-ebook *also available for free at any time for Kindle Unlimited subscribers
10. Join the 750+ AI Guild members to make the personal connection with peers www.theguild.ai
11. On the AI Guild team effort for #datalift No 4 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 750+ 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 4 on 26 March 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 and 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, Dânia Meira and Chris Armbruster
12. 11:15 AM (CET) · Stage Find him on LinkedIn
13. 12:00 PM (CET) · Stage Find her on LinkedIn
14. Use cases in production · 12:45 PM (CET) · Sessions
15. Julia Nitsch studied information and computer engineering at TU Graz, Austria. She received her M.Sc. in 2016 with a major in autonomous robotics. After her studies she worked in the Robotics and Perception Group, University of Zurich on urban search and rescue robotics. Since 2016 she is employed at Ibeo Automotive Systems GmbH and did her PhD studies in cooperation with the Autonomous Systems Lab, ETH Zurich. Moderated by Andrés Prada González
16. Dr. Edith Chorev’s background is in the field of system and computational neuroscience. She has a PhD from the Hebrew university of Jerusalem. She has worked as a researcher both at the Humboldt University and at the MPG for experimental medicine, focusing on how information is coded by neural networks and neural networks dynamics. Since then she worked as a freelance data scientist mainly with biotech early ventures and is now heading the data science efforts in FactoryPal. Moderated by Irem Nasir
17. Dr. Yernat Assylbekov received his PhD in Mathematics from the University of Washington, Seattle. Since then he held Postdoctoral Research positions at Northeastern University, Michigan State University and Rice University. Yernat is an experienced mathematician with an extensive list of publications. Also, he was actively involved in teaching various mathematics courses at the undergraduate and graduate levels. Moderated by Aline Quadros
18. As a Microsoft Digital Advisor, Andreas Kopp advises Enterprise customers on the planning and implementation of digital business solutions. His focus is on applied business AI solutions, including medical imaging and fraud detection. Furthermore, he specializes in practical solutions for the responsible use of AI systems. Among these are AI interpretability and fairness, as well as differential privacy. Moderated by Fabian Harder