04 - 05 October 2022

Enterprise AI Summit Enterprise AI Summit schedule

Berlin AI Summit



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  • 08:00

    REGISTRATION & LIGHT BREAKFAST

  • 09:00
    Vidya Munde-Mueller

    WELCOME NOTE & OPENING REMARKS

    Vidya Munde-Mueller - Director - Founder Institute

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    Vidya is very passionate about new technologies like AI, Blockchain, AR/VR, Internet of Things etc. As a Women-AI-Ambassador, she wanted to motivate more women to work in new technologies in order to level the playing field. In her previous roles at Deutsche Telekom as Product Manager and AI-Evangelist, she created awareness about AI and took part in numerous activities and projects to foster it's acceptance within the company. Together with other colleagues, she was responsible to kick-start a cross-unit AI Community with an interdisciplinary team. Vidya's determination to work in AI was born during her stay in the Silicon Valley, where she was responsible as Business Manager to find AI startups for different use cases.

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  • CURRENT LANDSCAPE

  • 09:15
    Max Sommerfeld

    Opportunities and Challenges of AI - A Banking Perspective

    Max Sommerfeld - Vice President in Data AI & API Engineering - Deutsche Bank

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    Opportunities and Challenges of AI

    Banking has long been a highly virtual business – the opportunities for saving costs and increasing revenues through AI are immense. At the same time, high levels of regulation and expectations of business continuity pose unique challenges to realizing these benefits. In this presentation, I will discuss the application of AI in banking, including typical data sets and use-cases, models and methods, challenges and solutions.

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  • 09:40
    Philipp Kanehl

    Infering Cause from Effect - A Weight Prediction Case Study

    Philipp Kanehl - Data Scientist - Oviva

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    Inferring Cause from Effect - A Weight Prediction Case Study

    Recent years have substantiated the efficacy of blended care approaches in various medical fields. Particularly, people with obesity and diabetes can benefit from automated dietary interventions due to the availability of in-app bodyweight tracking and AI-supported meal monitoring.

    We propose a causal structural model of weight change dynamics inspired by physiological science. This fully interpretable model allows drawing causal links of meal composition and activities tracked by our users, to weight change. We show how the trained parameter space becomes a utile tool in generating predictions and designing interventions, helping patients reach their weight goals.

    Philipp Kanehl is a Bayesian enthusiast and a Staff Data Scientist of the Oviva AG. Before migrating to health-tech he worked as a data science consultant with IBM. Philipp holds a PhD in Theoretical Physics from the TU Berlin.

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  • 10:05
    Kshetrajna Raghavan

    Using Interconnected ML Models to Tackle Retail Challenges

    Kshetrajna Raghavan - Staff Data Scientist - Shopify

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    Using Interconnected ML Models to Tackle Retail Challenges

    Running a retail business is hard. At Shopify, we have millions of merchants of varying sizes that face many challenges, including: • Selling across multiple online channels and managing product metadata, like categories, which is vital for search relevancy and discoverability • Scaling a business while dealing with an ever changing online ad space with new regulations In his talk, Kshetrajna Raghavan will present a holistic view of how Shopify uses interconnected ML systems to solve retail obstacles for Shopify merchants, all while giving these entrepreneurs of all sizes a competitive advantage. Kshetrajna will talk about the tools and platforms Shopify built to support these models, how they evolve continuously, and how they can be applied.

    Kshetrajna is a Staff Data Scientist at Shopify working on the capital algorithms team. He has built and productionalized many models in various domains including retail, ad-tech and healthcare. His interests are mainly applied ML and ML systems. Outside of work, Kshetrajna loves to spend time with his dogs, play music on his guitar, and is an avid gamer.

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  • 10:30

    COFFEE & NETWORKING BREAK

  • SUPPORTING AI IN ENTERPRISE

  • 11:00
    Carmen Martínez

    Seven Best Practices for a Conversational IVR

    Carmen Martínez - Conversational UX Expert - FlixBus

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    Seven Best Practices for a Conversational IVR

    With Twilio, FlixBus is being able to transition its customer service hotlines from a legacy IVR, where even minor changes often required weeks of effort, to a modern one where the flexibility of cloud APIs allows for optimized metrics and customer satisfaction.

    In this talk, we present seven best practices for improving the caller experience by applying a set of Conversational UX principles to call center automation. By designing for self-service and utilizing caller-centric vocabulary, system-like politeness, structured short responses, and natural language recognition for menu navigation, we are being able to create caller-friendly experiences that equally foster customer satisfaction and meet our automation goals.

    Dr. Carmen Martinez is a Conversation Analyst and Ethnographer of Communication working in Conversational Artificial Intelligence at FlixBus. As an expert in human-to-human conversation, she contributes to a cross-disciplinary team by automating customer service interactions, modelling both text- and voice-based human-to-machine conversations, and developing visual solutions for graphical and multimodal conversational agents. Carmen holds a PhD in Conversation Analysis and is the author of “Conversar en español: un enfoque desde el Análisis de la Conversación” published by Peter Lang Berlin.

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  • 11:25
    Stefan Wellsandt

    Augmenting Data Analytics in Manufacturing with a Digital Assistant

    Stefan Wellsandt - Research Associate - BIBA

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    Augmenting Data Analytics in Manufacturing with a Digital Assistant: Insights from the COALA project

    The shortage of skilled workers is a barrier to applying data analytics. Augmented analytics is an approach to lower it by using machine learning to automate related activities and natural language applications to assist less-skilled employees. This talk will present a related case study from the white goods industry. It focuses on a quality test lab at the end of a production line where workers use a digital assistant prototype to interact with descriptive and predictive data analytics

    Stefan works as a research associate at the institute for production and logistics in Bremen (BIBA), Germany. His background is in industrial and mechanical engineering. Over the last ten years, he acquired expertise in information management and computer science, while working in various international and inter-disciplinary research projects. Stefan currently coordinates the Horizon 2020 research project COALA. Its goal is to build and test trustworthy, voice-enabled digital assistants for manufacturing

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  • 11:50
    Salma Bakouk

    The Concept of Data Observability

    Salma Bakouk - Co-Founder & CEO - Sifflet

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    The Concept of Data Observability

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  • 12:15
    Muslim Elkotob

    Real-World AI/ML Use-Cases and Performance Modeling

    Muslim Elkotob - Principle Solutions Architect - Vodafone

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    Assessing Real-World ML Performance

    With Artificial Intelligence and Machine Learning gaining ground and playing an increasingly important role in industry verticals use cases and Beyond 5G scenarios, we take a closer look in this presentation at AI and ML from that perspective. To enable any stakeholder in the digital ecosystem to better interact with AI and ML modules and systems, we present our ETSI Generic Autonomic Network Architecture (GANA) with multi-layer autonomics. Then we touch upon behavioral modelling and workflow shaping for a set of use cases and business scenarios using GANA and AI+ML. Use-cases and scenarios we will cover in more depth include: Telco Data Space with Data Sharing and Trusted AI, Marketplace-based Models with AI-based Testing and Certification, and Knowledge-Plane driven cognitive networking for MEC.

    Profound experience in designing, negotiating and evaluating KPIs, CSFs, SLAs; strategic roadmap and product design/ development; project acquisition, tenders, market analysis & business development, strategic positioning & benchmarking, Telco/IT solutions architectural design & pilot realization, business case development and realization incl. CAPEX&OPEX optimization

    Additionally Distinguished for: - International background (incl. full command of 7 languages, successful lead/completion of large multi-national projects), intercultural competence, and very strong communication/presentation skills - Community/consortium building and stakeholder management capabilities & having a powerful network of trusted partners and contacts throughout the IT/Telco ecosystem - High level of technical expertise in IT/Telco industry processes and systems and in solving complex problems

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  • 12:55

    LUNCH

  • CONSIDERATIONS FOR IMPLEMENTING AI

  • 14:05
    Vinoth Kannan

    The Daimler Truck Journey Towards Data Products

    Vinoth Kannan - Product Owner - Daimler Trucks

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    The Daimler Truck Journey Towards Data Products

    For the vast majority of organizations, investments made on AI have not led to meaningful performance improvements. Companies still struggle to scale promising Data science proof-of-concepts. One of the main reasons is due to the cultural aspect of how the company is organized around data. By focusing on technical capability of data management which are infrastructure driven - acquiring, storing and consuming data, has diminished the focus from the higher objective – treating data as a product. At Daimler Truck, a data product is treated as a data asset, which is optimized for consumption. To handle such data products we had to reinvent our data platform, data management capability, policies and our overall data strategy- that includes embracing new architectural paradigms such as data mesh and most of all empowering the organization to be data product driven. This talk walks the audience through this journey and provides some key insights on the dimensions of People, Process and Product.

    Vinoth is an accomplished Data & Cloud Architect and Product Leader, with over a decade of strategic and hands-on experience in building large scale distributed systems, Big Data and Cloud platforms & solutions. As a facilitator and builder of world-class technology products and teams, Vinoth has a track record of strategizing, designing and implementing multiple Advanced Analytics platforms and building high performance teams in a green-field environment.

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  • 14:35
    Sammer Puran

    Journey From Small Experimentational Cases to MLOps - Setting a New Pillar in the AI Strategy at Swiss Post CH AG

    Sammer Puran - Machine Learning Engineer - Swiss Post

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    AI-Strategy from Swiss Post

    How can you move from experimental level to productive use cases? in this presentation I will provide use cases in image recognition and assess challenges and best practices.

    Sammer has completed his Masters in Computer Science with Focus on Machine Learning and is currently working in the Swiss Post as Data Scientist. His interests lie in designing Machine Learning applications and being part of how they will shape our future. Applications of Machine Learning, which help us in our every day lives fascinate me to a great extent.

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  • 15:05

    COFFEE & NETWORKING BREAK

  • MANAGING AI

  • 15:50
    Andre Meyer-Vitali

    Trustworthy Hybrid AI

    Andre Meyer-Vitali - Senior Researcher - DFKI

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    Trustworthy Hybrid AI

    While in recent years AI systems have shown an increased performance at solving complex tasks, these systems still suffer from a lack of reliability, robustness, transparency and other related problems that question their trustworthiness. A trend that aims at improving the trustworthiness of these systems is hybrid AI that will be introduced with a taxonomy and modular design patterns for describing and designing trustworthy AI systems to unify statistical (data-driven) and symbolic (knowledge-driven) methods. We will also dive into graph-representation and multi-agent learning and reasoning in order to give some examples and a context in which the patterns can be used

    Dr. André Meyer-Vitali is a computer scientist who got his Ph.D. in software engineering and distributed AI from the University of Zürich. He worked on many applied research projects on multi-agent systems at Philips Research and TNO (The Netherlands) and participated in AgentLink. He also worked at the European Patent Office. Currently, he is a senior researcher at DFKI (Germany) focused on engineering and promoting Trusted AI and is active in the AI networks TAILOR and CLAIRE. His research interests include Software und Knowledge Engineering, Design Patterns, Hybrid Neuro-Symbolic AI, Causality, and Agent-based Social Simulation (ABSS) with the aim to create Trust by Design.

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  • 16:15

    Panel: Implementing Robust AI Methodology at All Levels

  • Stathis Grigoropolous

    MODERATOR

    Stathis Grigoropolous - Data Scientist - Booking.com

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    Panel: Implementing Robust AI Methodology at All Levels.

    Experienced Information Engineer seeking to optimize Big Data systems and implement Artificial Intelligence across all Businesses. Eager to design Machine and Deep Learning methodologies, manage projects, share knowledge and best practices with the team. Able to use advanced techniques to increase the speed and scale of software engines. Created sales predictive models combining Machine Learning and Time Series models to increase forecasting accuracy. Quick learner, team player, highly motivated and not losing sight of the details under pressure.

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  • Varun Kohli

    PANELIST

    Varun Kohli - Lead Engineer - Machine Learning - Google

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    Deep Learning to detect Intrusion Attempts

    Machine learning and Artificial Intelligence are one of the hottest topics in computer science today. Many problems deemed ""impossible"" only five years ago have now been solved by machine learning: playing GO, recognizing what is in an image, or translating languages. Join us as we lift the lid on popular network attacks of 2022 and dive deep into the world of deep learning to understand how models can detect them proactively.

    Varun is a mountaineer and an engineer by heart, currently working as a machine learning engineer with Google and builds solutions to drive security insights for Alphabet entities. Along with his passion for high altitude mountain climbing, he is an avid machine learning developer, researcher and a public speaker. Google Crowdsource, Amdocs R&D, c0c0n, NASSCOM WWRT, Nullcon, and Nagarro DevOpsCon are to name a few.

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  • Sarah Haq

    PANELIST

    Sarah Haq - Senior Machine Learning Engineer - Artsy

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    Recommending Artworks: The Importance of Understanding the Collector's Profile

    The art market is an extremely complex industry, with even more complex collectors. Our recommendations need to be good enough to convince someone to make a massive financial investment, but how do we use machine learning algorithms to re-create the emotional connection a collector has when adding a piece to their collection? In this talk, I will discuss how I went about building recommendation models at Artsy to help collectors discover and buy art they truly love.

    Sarah is a Senior Machine Learning Engineer at the world's largest online art marketplace, Artsy, and a Lecturer at the Karlsruhe University of Applied Sciences and Leuphana University. She has over ten years of experience working with data and building machine learning models for various startups, from underwear companies to unicorns. She is currently shaping the personalisation strategy at Artsy and building a recommendation engine to connect collectors with artworks they will love. The art market is an extremely complex industry, with even more complex collectors. The recommendations need to be good enough to convince someone to make a massive financial investment, but how do we use machine learning algorithms to re-create the emotional connection a collector has when adding a piece to their collection? Sarah will be presenting at our Enterprise AI Summit in Berlin on 4-5 October to address this question.

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  • Prokopis Gryllos

    PANELLIST

    Prokopis Gryllos - Senior Data Scientist - Shopify

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    Prokopis is a product-minded Data Scientist who enjoys Economics, Finance, and Algorithms

    Skills: data science, programming, product development, distributed systems Academic: statistics, machine learning, economics, game theory, social network analysis

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  • 17:00

    NETWORKING RECEPTION

  • 18:00

    END OF DAY ONE

  • 08:00

    REGISTRATION & LIGHT BREAKFAST

  • 09:00
    Vidya Munde-Mueller

    WELCOME NOTE & OPENING REMARKS

    Vidya Munde-Mueller - Director - Founder Institute

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    Vidya is very passionate about new technologies like AI, Blockchain, AR/VR, Internet of Things etc. As a Women-AI-Ambassador, she wanted to motivate more women to work in new technologies in order to level the playing field. In her previous roles at Deutsche Telekom as Product Manager and AI-Evangelist, she created awareness about AI and took part in numerous activities and projects to foster it's acceptance within the company. Together with other colleagues, she was responsible to kick-start a cross-unit AI Community with an interdisciplinary team. Vidya's determination to work in AI was born during her stay in the Silicon Valley, where she was responsible as Business Manager to find AI startups for different use cases.

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  • AUTOMOTIVE

  • 09:15
    Eric JoAchim Liese

    From DevOps to MLOps: Building an End to End Pipeline for Fast, Simple & Reliable Training, Deployment and Serving of ML Application in Retail

    Eric JoAchim Liese - Lead Architect & Advisor for AI & Big Data - BSH Home Appliances

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    From DevOps to MLOps: Building End-to-End Pipeline for Fast, Simple & Reliable Deployment of ML in Retail

    While most companies are now familiar with the requirements of a good DevOps process, the situation is still not on the same level of maturity for end-to-end MLOps processes that are required to train, test, deploy, run and monitor ML models in production.

    Generally, MLOps can be considered as an extension of DevOps. The same principles that are valid for professional software development, also apply in the context of ML development, but the complexity is much higher.

    For complete reproducibility of the status of an ML product, at least two artefacts are needed in MLOps (versioned code & model), and ideally versioned data as a third artefact, while in DevOps only one is required (versioned code).

    Additionally, model training and optimization needs special tools for experiment tracking. And finally, the behaviour of productive models needs to be monitored with new concepts and tools, in contrast to traditional application monitoring.

    At BSH we designed a concept for a generic, modular end-to-end pipeline that provides components for all the necessary steps from initial data preparation, over training, experiment tracking, testing, deployment, running and monitoring in production.

    About: After receiving his Degree In Mathematics and Computer Science with focus on Machine Learning and AI, Eric worked as a Senior Data Scientist, ML Engineer and Consultant for several years. He designed concepts to automate the development of AI products by creating End-to-End MLOps Pipelines, as well as strategies to help companies to become data- and AI-driven. Before that, he worked as a Software Engineer for over a decade and later as a Lead Developer on many projects.

    Eric is currently a Lead Architect & Advisor for AI & Big Data infrastructure, helping BSH to shape a strategy to become a data and AI driven company. His responsibilities also encompass designing Data Lakes of BSH, with his main focus on concepts for automating data ingestion, ETL, data quality and productionization of ML/AI processes (MLOps) on AWS.

    He is the author of the upcoming book “AI Business Transformation” https://book.ejl.ai/ , which will be published by the end of the year. LinkedIn: https://www.linkedin.com/in/achimliese/

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  • 09:50
    Clara Hennecke

    How to Perform Real-Time Sentiment Analytics on the Audience Messages

    Clara Hennecke - Streaming Advocate - Quix

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    How to Perform Real-Time Sentiment Analysis on the Audience Messages

    Building a ML model is challenging. Deploying it reliably into production is hard. And doing this with real-time streaming data brings even more complexity. That’s at least what you might have thought before joining this demo on how to perform a real-time sentiment analysis on the audience’s messages. Come and learn about streaming (vs batch), deploying ML models in real-time, and automating MLOps in a data science project. All you need to participate is your phone’s QR reader!

    Clara Hennecke is a Streaming Advocate at Quix, where she supports Quix' clients to get impactful projects around stream processing running. She was previously in the data team of the Venture Capital Investor Project A, advising it's portfolio start-ups in all topics data, ranging from their first data hire to scalable data architectures for hyper-growth ventures.

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  • RETAIL & HOSPITALITY

  • 10:05
    Tobias Hoelzer

    Apes, Babies, Tigers - How Does AI Shape the Future of Learning

    Tobias Hoelzer - Founder - vCoach

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    Apes Babies Tigers - How does AI shape the Future of Learning?

    The truth about Artificial Intelligence (AI) is that all of us are already using some form of it whether we realize it or not. Siri and Alexa, are best examples of voice-activated computers that grow more intelligent with machine learning. But also Netflix, Spotify, and Amazon all use AI to predict our interests, to match us with content that aligns best with what we’re looking for and what we’re most likely to enjoy. But can you imagine a training in which AI analyses your presentation skills and helps you present results more confidently to customers, superiors or the team? No?

    Tobias has successfully completed Software Projects involving millions of users and Terabytes of data. He built products, coded in a team, led teams, led projects, founded companies, raised money, hired employees and closed software deals. Based in Berlin.

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  • 10:40

    COFFEE BREAK

  • Recruitment

  • 11:25
    Mehdi Ouazza

    Scale your Data Before Scaling your Data Team: Hyper-Growth

    Mehdi Ouazza - Staff Data Engineer - Mehd.io

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    Scale your Data Before Scaling your Data Team: Hyper-Growth

    Hiring data talent is hard. How do you ensure your data infrastructure doesn’t fall apart while scaling your team?

    In this talk, Mehdio will share his experience working in a data team at a hyper-growth company. He will touch upon the major challenges he faced and give pragmatic tips on how to make sure you still have a business impact while scaling both your data team and infrastructure.

    Mehdi is a data geek entrepreneur passionate about Big data, Data Science, Web App, and Music. With more than 8 years of experience, he got the opportunity to work on multiple aspects of data, including data engineering, infrastructure, analytics, and machine learning, from corporate to scale-up environments.

    Since 2021, Mehdio has been putting a lot of effort into sharing his knowledge online through technical blogs, YouTube, and Podcasts. He also recently launched the Data Creators Club to help people find the best data mentors online.

    Continuous learning is his motto: sharing is caring.

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  • CUSTOMER SERVICE

  • 11:55
    Calvin  Seward

    You’re Probably Doing Reinforcement Learning Without Knowing It. Here’s How to do it Better.

    Calvin Seward - Research Scientist - Zalando

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    Why you're Already Doing Reinforcement Learning and a Few Tips on Doing it Better

    “We’d like to do reinforcement learning, but...” I hear it all the time from ML practitioners working in fields as diverse as search, pricing, real-time bidding and recommendations. The funny thing is by creating search results, prices and recommendations these practitioners are already “doing” reinforcement learning (RL). They are creating policies which react to observations and collect as much reward as possible. Since they’re already doing RL, the question is how they can do it better. Those are the two points I’ll cover in my talk. First I’ll work though real world examples showing RL is both a set of problems that data leaders commonly encounter and a set of solutions tailored to solve those problems. Then I’ll dive into one specific and easy way to solve RL problem: the contextual bandit. When my session is done, you’ll not only know that you’re doing RL, you’ll also know how to do it better.

    Calvin Seward is a Research Scientist for Zalando's Research Lab, working mainly on reinforcement learning problems. By treating problems as diverce as pricing, recommendations and search as reinforcement learning problems, solutions can continually learn more about our customers and our products. The main line of attack for these problems is deep neural networks written in the Torch framework and trained on GPU clusters. At the same time, he is involved in applying lessons from GPU-driven HPC computing and cutting edge Machine Learning to other fields of the Zalando universe. Past projects have included sizing solutions for online retail, warehouse management and recommendation systems.

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  • PERSONALISATION

  • 12:30
    Sarah Haq

    Recommending Artworks: The Importance of Understanding the Collector's Profile

    Sarah Haq - Senior Machine Learning Engineer - Artsy

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    Recommending Artworks: The Importance of Understanding the Collector's Profile

    The art market is an extremely complex industry, with even more complex collectors. Our recommendations need to be good enough to convince someone to make a massive financial investment, but how do we use machine learning algorithms to re-create the emotional connection a collector has when adding a piece to their collection? In this talk, I will discuss how I went about building recommendation models at Artsy to help collectors discover and buy art they truly love.

    Sarah is a Senior Machine Learning Engineer at the world's largest online art marketplace, Artsy, and a Lecturer at the Karlsruhe University of Applied Sciences and Leuphana University. She has over ten years of experience working with data and building machine learning models for various startups, from underwear companies to unicorns. She is currently shaping the personalisation strategy at Artsy and building a recommendation engine to connect collectors with artworks they will love. The art market is an extremely complex industry, with even more complex collectors. The recommendations need to be good enough to convince someone to make a massive financial investment, but how do we use machine learning algorithms to re-create the emotional connection a collector has when adding a piece to their collection? Sarah will be presenting at our Enterprise AI Summit in Berlin on 4-5 October to address this question.

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  • 12:55

    LUNCH

  • CROSS INDUSTRY IMPLEMENTATION

  • 13:55
    Ertuğrul Dalboy

    Bridging the Gap from Model to Delivery

    Ertuğrul Dalboy - Manager Machine Learning Engineering - ING

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    Bridging the gap from Modelling to Delivery

    We realized that developing models is not enough to create an impact, if there is no way of using them or if there is a gap in the skillset to go to production. In this talk, I am going to cover how we changed our structure and way of working to create an analytics delivery team.

    Ertugrul leads an analytics team which is part of a global analytics organization called ING Analytics. He studied Computer Science at Istanbul Technical University. For the last 5 years he has been working for ING in different roles in Analytics and most recently as Machine Learning Engineering Manager.

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  • 14:20

    Panel: What is the Future of AI Adoption?

  • Elizabeth Press

    MODERATOR

    Elizabeth Press - Blogger & Director of Data - Babbel & D3M Labs

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    Panel: What is the Future of AI Adoption?

    Elizabeth is a data leader, avid blogger and published author with a track record of building high-performing data organizations and creating strategic advantages out of data.

    She has architected robust and impactful organizations, run analytics and strategy projects, as well as provided insights for blue chips and top investors on every continent. A globally minded leader and entrepreneur who has lived and professionally worked in 6 countries across 3 continents, she can manage diversity and inspire cross-functionally - and virtually.

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  • Balavivek Sivanantham

    PANELIST

    Balavivek Sivanantham - Technical Lead/Machine Learning Engineer - Bayer

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    Balavivek is a Data Engineer who loves to play and work with Data. He recently developed a data science tool using Audi Assembly line data that let workers in the assembly line to produce cars with few to zero errors. He'd love to combine his passion for data and machine learning with his data engineering skills to continue building personalized tools to help people and businesses by reducing the cost with predictive analysis.

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  • Asma Zgolli

    PANELIST

    Asma Zgolli - Machine Learning Engineer - Centa AG

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    Panel: What is the Future of AI Adoption?

    Asma is a Doctor and Engineer in Computer Science with a specialization in Big Data and Applied Machine Learning. Her goal is to be able to make use of her data science and analytics skills to solve real-world problems.

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  • 15:00

    END OF SUMMIT

Berlin AI Summit

Berlin AI Summit

04 - 05 October 2022

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