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ML BERLIN HYBRID CONFERENCE: Learn more about on-site or online attendance here

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ML BERLIN HYBRID CONFERENCE: Learn more about on-site or online attendance here

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ML SINGAPORE ONLINE CONFERENCE: Learn more about online attendance here

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ML BERLIN HYBRID CONFERENCE: Learn more about on-site or online attendance here

Sessions

MLCON
The Event for ML Technologies & Innovations
November 28 – December 2, 2022 | Berlin or Online

Sessions

Session Type Icon

k-NN on steroids – an introduction to approximate nearest neighbours

Kacper Łukawski, Qdrant
Session Type Icon

Machine Learning in a Browser

Athan Reines, Quansight
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Company valuation using deep learning

Prof. Subramanyam Chandrasekhar, Jagdish Sheth School of Management
Session Type Icon

Software 2.0: How does data become an AI software artifact?

Leonard Püttmann, onetask.ai // kern.ai
Session Type Icon

Have you really embraced audio?

Benjamin McCulloch, Conversation Designer
Session Type Icon

Future of HR from 2021: Reimagining the Future.

Dr. Raul Rodríguez, Woxsen University
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Threat modeling and security assessment for ML models

Alex Polyakov, Adversa.AI
Session Type Icon

How Machine Learning is (becoming) creative

Andreas Bühlmeier, DBC Enterprice Intelligence
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Practical Experiences from setting up the complete MLOps pipeline

Prof. Dr. René Brunner, Datamics / Professor an der Hochschule Macromedia
Eric Joachim Liese, BSH Home Appliances Group
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State-of-the-art in large scale recommender systems

Aditya Gulgani, Meta
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Case Studies: Machine Learning in Web Applications

Athan Reines, Quansight
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Doing More with Less: Building Machine Learning Solutions without Large Labeled Datasets

Jesus Rodriguez, Invector Labs
Session Type Icon

Playing Doom with TF-Agents and TensorFlow 2.0

Andreas Eberle, arconsis IT-Solutions GmbH
Session Type Icon

Using Neural Networks for Natural Language Processing

Christoph Henkelmann, DIVISIO GmbH
Session Type Icon

Scaling Machine Learning Systems up to Billions of Predictions per Day

Carmine Paolino, Alan Turing Institute
Session Type Icon

Applying machine learning online at scale

Jon Bratseth, Yahoo
Session Type Icon

Towards a Convenient ML Infra

Vadim Markovtsev, source{d}
Session Type Icon

Understanding how Neural Networks work by looking at the Low Level API of TensorFlow 2

Oliver Zeigermann, OPEN KNOWLEDGE GmbH
Session Type Icon

Streamlining your Machine Learning Projects using MLOps with Azure Machine Learning

Sam Vanhoutte, Codit
Session Type Icon

Stopping Public Transport Coronavirus Infections with Big Data

Dr. Tim Frey, iunera GmbH & Co. KG
Session Type Icon

Asynchronous and version-controlled data science with Jupyter notebooks

Juha Kiili, Valohai
Session Type Icon

Why Security is Important in ML and How to Secure your ML-based Solutions

Dr. Rachid Kherrazi, AKKA Netherlands B.V.
Session Type Icon

Dockerize, Standardize, Deploy and Scale your own Machine Learning Model

Jens Beyer, LAVRIO.solutions GmbH
Session Type Icon

Leveraging Reinforcement Learning for Improvement of Traditional Controllers for a Vehicle Model – A Case Study

Christoph Stockhammer, The MathWorks GmbH
Session Type Icon

Some Things I Wish I Had Known about Scaling Machine Learning Solutions

Jesus Rodriguez, Invector Labs
Session Type Icon

Machine Learning on the Edge using TensorFlow Lite

Håkan Silfvernagel, Miles Oslo AS
Session Type Icon

Continuous Delivery for Machine Learning Applications with Open Source Tools

Daniel Schruhl, ThoughtWorks Germany
Christoph Windheuser, Databricks
Session Type Icon

Pre-Moderation – The Safe Sandbox for your Community

Christian Dedié, gutefrage.net GmbH
Dr. Katsiaryna Stalpouskaya, gutefrage.net GmbH
Session Type Icon

Vector embedding AI in production systems

Jon Bratseth, Yahoo
Session Type Icon

Privacy-preserving Machine Learning

Nicolas Kuhaupt, Fraunhofer IEE
Session Type Icon

Efficient Transformers

Christoph Henkelmann, DIVISIO GmbH
Session Type Icon

Client-side Artificial Intelligence in JavaScript using Tensorflow.js

Mathias Burger, TNG Technology GmbH
Session Type Icon

Enhancing Page Visits by Topic Prediction

Dieter Jordens, Dieter Jordens IT Consulting BV
Session Type Icon

How We Covered Concept Drifts In Time Series Forecasting For Public Transport

Dr. Tim Frey, iunera GmbH & Co. KG
Session Type Icon

The Bigger the Better for NLP – or not?

Dr. Maximilian Junker, Qualicen
Session Type Icon

Kotlin? For Machine Learning?

Hauke Brammer, DeepUp GmbH
Session Type Icon

All You Need to Know now about (Deep) Reinforcement Learning

Andreas Bühlmeier, DBC Enterprice Intelligence
Session Type Icon

Lean Data Science: Tailoring Agile Practices for Data Science Projects

Askhat Urazbaev, ScrumTrek
Session Type Icon

Unsupervised Category-Specific Mesh Reconstruction: A Future ML Opportunity

Thomas Chen, Academy for Mathematics, Science, and Engineering
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Machine Learning Sucks

Dr. Pieter Buteneers, Sinch
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Case Study: Making the Most out of a Doomed Project

Vladimir Rybakov, WaveAccess
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Federated Learning: The Next Generation Machine Learning for Regulated Domains – Healthcare and Banking

Prof. Debjyoti Paul, Amazon
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Data Centric AI vs Model Centric AI – The “right” data vs “best” model culture

Prof. Debjyoti Paul, Amazon
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MLOps: DevOps for Machine Learning

Hauke Brammer, DeepUp GmbH
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How are AI and ML helping to boost our agile development process

Dr. Rachid Kherrazi, AKKA Netherlands B.V.
Session Type Icon

Algorithmic Diversity & Nonbinarity. How to Reimagine Ability, Tech & Ethics

Yonah Walker, Yonah Fund
Session Type Icon

Dataset design for technically and legally clean ML development

Michael Osterrieder, vAIsual Inc
Session Type Icon

From Paper to Product – How we implemented BERT

Christoph Henkelmann, DIVISIO GmbH
Session Type Icon

AMA (Ask Me Anything) Session

Dr. Pieter Buteneers, Sinch
Christoph Henkelmann, DIVISIO GmbH
Sebastian Meyen, Software & Support Media
Session Type Icon

3D Human Pose Estimation: How To Use It in AI Coach Apps

Maksym Tatariants, MobiDev
Session Type Icon

Nature inspired optimization algorithms / Algorithm-based Staffing with Ant Colony Optimization

Laura Kölbl, MATHEMA GmbH
Session Type Icon

Increasing Business Efficiency through NLP Integration: Text Summarization and Sentiment Analysis

Anastasiia Vynychenko, MobiDev
Session Type Icon

Speeding up Last-Mile Delivery with AI

Samar Singla, Jungleworks
Session Type Icon

The Future of People

Jeff Blankenburg, Amazon Alexa
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Affective Computing – bringing humans and machines closer through emotions

Håkan Silfvernagel, Miles Oslo AS
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Voice assistants with the Fraunhofer SPEAKER project

Jochen Emig, ONSEI GmbH
Leonard Füchsel, ONSEI GmbH
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No prejudice. No remorse. Unsupervised ML

Liudmyla Taranenko, MobiDev
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Voice Development with modern AI Tools

Jochen Emig, ONSEI GmbH
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The Rise of Voice Commerce in India

Kumar Rangarajan, Slang Labs
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Data as a Product: How To Develop Good Data Products

Bernd Fondermann, bernd fondermann brainlounge
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Thinking outside the box: Insights From the Visually-Impaired Community into Audio Games Development

Jo Jaquinta, TsaTsaTzu
Session Type Icon

What about the Kids? How Speech Technology transforms Digital Experiences for Kids

Dr. Patricia Scanlon, SoapBox Labs
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How Voice Search is Evolving: Using Insights from Alexa Voice Search

John Campbell, Rabbit & Pork
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Creating a testing workbench for ML teams using just Python

Darshita Chaturvedi, Atri Labs
Session Type Icon

Machine Learning outcome in Health Sector in Bangladesh

Dr. Zoha Rahman, Centre For Big Data & Machine Learning
Session Type Icon

Demystifying Data Science

Grishma Jena, IBM
Session Type Icon

How to write a good Training Script?

Christoph Henkelmann, DIVISIO GmbH
Session Type Icon

Brewing Beer with the Help of IoT: Building a DIY Fermentation Sensor

Sven Haiges, SAP Hybris
Session Type Icon

Leveraging Earth Observation Satellite images for Financial Industry

Fekete Csongor, HolistiCRM Kft.
Session Type Icon

Holistic Voice Application Monitoring: How to sleep well every night

Dominik Meißner, 169 Labs GmbH
Session Type Icon

IoT and eSports – Learning for the industry in your spare time

Dr. Tobias Brambach, Point 8 GmbH
Session Type Icon

Let’s learn R by debugging the Currywurst!

Mihailo Joksimovic, Microsoft
Session Type Icon

How to use Natural Language Processing for automatic test case design?

Jannik Fischbach, Qualicen Gmbh
Session Type Icon

So, what exactly is MLOps?

Mihailo Joksimovic, Microsoft
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Welcome & Introduction

Sebastian Meyen, Software & Support Media
Session Type Icon

Voice Assistants for Customer Support based on Deep Learning

Thomas Abend, VITAS.ai
Session Type Icon

Delivering Rocketfuel – Pipelines für Deep Learning mit Small Data

Hauke Brammer, DeepUp GmbH
Session Type Icon

How to Define and Execute a Successful Data & AI Strategy

Karthik Bharadwaj, H2O AI
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Data science without data: the cold start playbook

Alex Honchar, Neurons Lab
Session Type Icon

Predictive Maintenance – How Does Data Science Revolutionize the World of Machines?

Victoriya Kalmanovich, Navy
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Bots testing Bots: From manual to automated testing for conversational AI

Christoph Börner, Botium GmbH
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Deep Learning: the final Frontier for Time Series Analysis?

Oleksandr Honchar, Neurons Lab / Mawi Solutions
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AI-powered content moderation

Stefano Tempesta, Microsoft
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An Introduction to Transformers

Christoph Henkelmann, DIVISIO GmbH
Session Type Icon

Machine learning in the browser with TensorFlow.js

Håkan Silfvernagel, Miles Oslo AS
Session Type Icon

MLOps – from ML model to cloud-service: challenges and pitfalls of scalable document processing

Ward Van Laer, Ixor
Session Type Icon

Kubeflow on a Bare-Metal GPU Cluster from Scratch

Markovtsev Vadim, Athenian
Session Type Icon

Keyword Search is Dead! And so are Solr and Elasticsearch?

Daniel Wrigley, SHI GmbH
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Architectures that power Machine Learning

Bence Faludi, dataleio
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NLP for career progression: connecting people with jobs and courses

Gabrielle Fournet, Boostrs
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Natural Language Processing (NLP): The Transformer Zoo

Dr. Christian Winkler, datanizing GmbH
Oliver Zeigermann, OPEN KNOWLEDGE GmbH
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Production nightmare of building AI systems at scale – The last mile

Dr. Danish Rafique, Bayer AG
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Evaluating machine learning solutions for subjective problems

Robbe Sneyders, ML6
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Keep an eye on AI — Monitor ML models in production

Hauke Brammer, DeepUp GmbH
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Advances in Machine Learning Systems – from GPUs to Quantum

Andreas Bühlmeier, DBC Enterprice Intelligence
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Self-supervised Learning: Teaching AI common sense

Dr. Pieter Buteneers, Sinch
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Polars: Blazingly fast DataFrames

Ritchie Vink, Xomnia BV
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Human and Multi-agent systems

Dr. Neda Navidi, ETS
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Natural language processing for medical data

Anja Pilz, damedic
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Greenfield and Brownfield Data Labeling – from Idea to Production with data-centric AI

Johannes Hötter, onetask.ai // kern.ai
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AI-powered learning recommendation system: a map and match approach

Khieu Nguyen, Visier Inc.
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AIoT – Die Verbindung zwischen AI und IoT beherrschen!

David Monzel, mm1
Dr. Dirk Slama, Bosch Software Innovations
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How AutoML can give companies, with few data scientists, a key advantage

Ioannis Tsamardinos, JADBio
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Robo programmers? Competent minions? Or just sensational demos and hype?

Mohamed Sayed, Heuro Labs GmbH
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Optimize your network inference time as a data scientist, not as a web developer

Adrian Boguszewski, Intel
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Image Recognition: Past, Present and Future

Oliver Zeigermann, OPEN KNOWLEDGE GmbH
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Practical ML with Power BI Desktop

Markus Ehrenmüller-Jensen, Savory Data
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Underspecified pipelines: Why good models underperform in production

Nikolay Manchev, Domino Data Lab
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IoT Analytics at Scale – Customer based product development

Danilo Dresen, taod Consulting GmbH
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Understanding Artificial Intelligence: Explainable AI with Interpretable KPI Labels

Dr. Rudolf Felix, PSI FLS Fuzzy Logik & Neuro Systeme GmbH
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AI with a devops mindset – experimentation, sharing and easier ML deployment

Thiago de Faria, LINKIT
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MLOps, Automated Machine Learning Made Easy

Jan Mulkens, Ahead with Data
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Machine Learning: The Bare Math Behind Libraries

Piotr Czajka, TomTom
Łukasz Gebel, TomTom
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On-device machine learning – Core ML vs Create ML

Radu Dan, Deloitte Digital Romania
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How We Used Machine Learning to Detect Stress among our Berlin Colleagues

Matthias Hofmaier, Novatec Consulting GmbH
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AI is eating Software – how Second Generation AutoML will replace Software Development

Prof. Adam Blum, Auger.AI
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Building smarter solutions with no expertise in machine learning

Laurent Picard, Google
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AI Red Teaming: a facial recognition case study

Alex Polyakov, Adversa.AI
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AutoML, the Good, the Bad and the Why

Jan Mulkens, Ahead with Data
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Data Analytics Platform

Sameer Paradkar, AtoS
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Real Machine Learning challenges that you’ve never faced at Kaggle

Vladimir Rybakov, WaveAccess
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Interpretable Machine Learning – Application to Finance and Banks

Prof. Debjyoti Paul, Amazon
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Predicting Hotel Cancellations With Machine Learning: Classification and Time Series Analysis

Michael Grogan, MGCodesandStats
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Developing a Match-Making Algorithm Between Customers and Go-Jek Products!

Gunjan Dewan, Go-Jek
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Real, Fake and Deepfake: How Deep Learning Messed up Customer (Re)Identification and How It Will Fix It

Dr. Luca Marchesotti, Sparkd
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Using A.I to make recommendations for career progression

Dorra Nouira, Boostrs
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Automatic Image Cropping for Online Classifieds: a Journey from a Master Thesis to Production

Alexey Grigorev, OLX Group
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Protecting AI Solutions From Attacks

Alex Polyakov, Adversa.AI
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Building Human-in-the-Loop Machine Learning Systems – Fashion 2.0

Steven Bourke, Outfittery
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Next generation AI: Emotional Artificial Intelligence based on audio

Dagmar Schuller, audEERING GmbH
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How to raise intelligent Chatbots, keep them smart and make them useful

Hans-Peter Kuessner, adesso AG
Jens Polster, adesso AG
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MediaPipe: Building real-time cross-platform (mobile, web, edge, desktop) Video Audio ML Pipelines

Ming Yong, Goolge Inc
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The unreasonable effectiveness of simple models

Julien Schuermans, ML6
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The importance and pitfalls of pseudonymization

Lisa Becker, ML6
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Building AI startups with minimum risks

Liubov Zatolokina, MobiDev
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Multi Tasking Deep Learning for Natural Language Processing – Transfer Learning

Prof. Debjyoti Paul, Amazon
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Transforming Business with AI – From Vision to Strategy to Execution

Dr. Tatyana Goldberg, Allianz
Dr. Guy Yachdav, Aiola
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Battle of the Languages: Java and Python compete to solve programming challenges

Michael Inden, Freelancer
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From 2D to 3D: Unsupervised Reconstruction in Computer Vision using U-CMR

Thomas Chen, Academy for Mathematics, Science, and Engineering
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Honey Bee Conservation using Deep Learning

Thiago da Silva Alves, JArchitects
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Honey, I shrunk the TinyML

Lars Gregori, SAP CX
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#An Introduction to Clustering Algorithms

Christoph Henkelmann, DIVISIO GmbH
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Smaller, Faster, Cheaper: CNN Optimisation Techniques in Object Detection

Ilia Lorin, MobiDev
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Let’s talk about Recommendation Systems!

Mihailo Joksimovic, Microsoft
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Preserve data privacy in Machine Learning with Confidential Computing

Stefano Tempesta, Microsoft
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The Phoenix Project, how simulations can create high performing teams

Jan Schilt, GamingWorks
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Real time Covid-19 detection with Audio AI

Pia Eggert, Freelancer
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Unsupervised Learning with Autoencoders

Christoph Henkelmann, DIVISIO GmbH
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Artificial Intelligence and Machine Learning

Surendran Selvaraju, Schlumberger
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A Few Million Words Later – Text Analytics for Software Quality Assurance in Practice

Dr. Maximilian Junker, Qualicen
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Coded Leadership: Developing Scalable Management in an AI-induced Quantum World

Dr. Raul Rodriguez, Woxsen University
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Warning: this is not another MLOps best practices talk

Amit Bendor, Artlist
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Machine Learning Techniques for Estimating Science and Properties of Materials

Dr. Beauty Pandey, WOXSEN University Hyderabad
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Introduction to Sentence Embeddings

Dr. Christian Winkler, datanizing GmbH
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Designing Conversational Chatbot for Digital Operators

Burak Aytan, TURKCELL
Dr. Ilknur Durgar, TURKCELL
Muhammed Güre, TURKCELL
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Vector embedding AI in practice

Jon Bratseth, Yahoo
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NLU in Other Languages

Burak Isikli, Akbank
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Satellite imagery analysis: Machine Learning to prevent environmental crisis

Vladimir Rybakov, WaveAccess
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AbadIA: How to build an AI from zero to learn to play and solve a tough game

Juantomás Garcia, Sngular People S.L
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An Introduction to Natural Language Generation

Christoph Henkelmann, DIVISIO GmbH
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Fast and accurate: AI-based video processing

Sergey Maximenko, MobiDev

All Sessions

Session Type Icon

k-NN on steroids – an introduction to approximate nearest neighbours

Kacper Łukawski, Qdrant
Session Type Icon

Machine Learning in a Browser

Athan Reines, Quansight
Session Type Icon

Company valuation using deep learning

Prof. Subramanyam Chandrasekhar, Jagdish Sheth School of Management
Session Type Icon

Software 2.0: How does data become an AI software artifact?

Leonard Püttmann, onetask.ai // kern.ai
Session Type Icon

Have you really embraced audio?

Benjamin McCulloch, Conversation Designer
Session Type Icon

Future of HR from 2021: Reimagining the Future.

Dr. Raul Rodríguez, Woxsen University
Session Type Icon

Threat modeling and security assessment for ML models

Alex Polyakov, Adversa.AI
Session Type Icon

How Machine Learning is (becoming) creative

Andreas Bühlmeier, DBC Enterprice Intelligence
Session Type Icon

Practical Experiences from setting up the complete MLOps pipeline

Prof. Dr. René Brunner, Datamics / Professor an der Hochschule Macromedia
Eric Joachim Liese, BSH Home Appliances Group
Session Type Icon

State-of-the-art in large scale recommender systems

Aditya Gulgani, Meta
Session Type Icon

Case Studies: Machine Learning in Web Applications

Athan Reines, Quansight
Session Type Icon

Doing More with Less: Building Machine Learning Solutions without Large Labeled Datasets

Jesus Rodriguez, Invector Labs
Session Type Icon

Playing Doom with TF-Agents and TensorFlow 2.0

Andreas Eberle, arconsis IT-Solutions GmbH
Session Type Icon

Using Neural Networks for Natural Language Processing

Christoph Henkelmann, DIVISIO GmbH
Session Type Icon

Scaling Machine Learning Systems up to Billions of Predictions per Day

Carmine Paolino, Alan Turing Institute
Session Type Icon

Applying machine learning online at scale

Jon Bratseth, Yahoo
Session Type Icon

Towards a Convenient ML Infra

Vadim Markovtsev, source{d}
Session Type Icon

Understanding how Neural Networks work by looking at the Low Level API of TensorFlow 2

Oliver Zeigermann, OPEN KNOWLEDGE GmbH
Session Type Icon

Streamlining your Machine Learning Projects using MLOps with Azure Machine Learning

Sam Vanhoutte, Codit
Session Type Icon

Stopping Public Transport Coronavirus Infections with Big Data

Dr. Tim Frey, iunera GmbH & Co. KG
Session Type Icon

Asynchronous and version-controlled data science with Jupyter notebooks

Juha Kiili, Valohai
Session Type Icon

Why Security is Important in ML and How to Secure your ML-based Solutions

Dr. Rachid Kherrazi, AKKA Netherlands B.V.
Session Type Icon

Dockerize, Standardize, Deploy and Scale your own Machine Learning Model

Jens Beyer, LAVRIO.solutions GmbH
Session Type Icon

Leveraging Reinforcement Learning for Improvement of Traditional Controllers for a Vehicle Model – A Case Study

Christoph Stockhammer, The MathWorks GmbH
Session Type Icon

Some Things I Wish I Had Known about Scaling Machine Learning Solutions

Jesus Rodriguez, Invector Labs
Session Type Icon

Machine Learning on the Edge using TensorFlow Lite

Håkan Silfvernagel, Miles Oslo AS
Session Type Icon

Continuous Delivery for Machine Learning Applications with Open Source Tools

Daniel Schruhl, ThoughtWorks Germany
Christoph Windheuser, Databricks
Session Type Icon

Pre-Moderation – The Safe Sandbox for your Community

Christian Dedié, gutefrage.net GmbH
Dr. Katsiaryna Stalpouskaya, gutefrage.net GmbH
Session Type Icon

Vector embedding AI in production systems

Jon Bratseth, Yahoo
Session Type Icon

Privacy-preserving Machine Learning

Nicolas Kuhaupt, Fraunhofer IEE
Session Type Icon

Efficient Transformers

Christoph Henkelmann, DIVISIO GmbH
Session Type Icon

Client-side Artificial Intelligence in JavaScript using Tensorflow.js

Mathias Burger, TNG Technology GmbH
Session Type Icon

Enhancing Page Visits by Topic Prediction

Dieter Jordens, Dieter Jordens IT Consulting BV
Session Type Icon

How We Covered Concept Drifts In Time Series Forecasting For Public Transport

Dr. Tim Frey, iunera GmbH & Co. KG
Session Type Icon

The Bigger the Better for NLP – or not?

Dr. Maximilian Junker, Qualicen
Session Type Icon

Kotlin? For Machine Learning?

Hauke Brammer, DeepUp GmbH
Session Type Icon

All You Need to Know now about (Deep) Reinforcement Learning

Andreas Bühlmeier, DBC Enterprice Intelligence
Session Type Icon

Lean Data Science: Tailoring Agile Practices for Data Science Projects

Askhat Urazbaev, ScrumTrek
Session Type Icon

Unsupervised Category-Specific Mesh Reconstruction: A Future ML Opportunity

Thomas Chen, Academy for Mathematics, Science, and Engineering
Session Type Icon

Machine Learning Sucks

Dr. Pieter Buteneers, Sinch
Session Type Icon

Case Study: Making the Most out of a Doomed Project

Vladimir Rybakov, WaveAccess
Session Type Icon

Federated Learning: The Next Generation Machine Learning for Regulated Domains – Healthcare and Banking

Prof. Debjyoti Paul, Amazon
Session Type Icon

Data Centric AI vs Model Centric AI – The “right” data vs “best” model culture

Prof. Debjyoti Paul, Amazon
Session Type Icon

MLOps: DevOps for Machine Learning

Hauke Brammer, DeepUp GmbH
Session Type Icon

How are AI and ML helping to boost our agile development process

Dr. Rachid Kherrazi, AKKA Netherlands B.V.
Session Type Icon

Algorithmic Diversity & Nonbinarity. How to Reimagine Ability, Tech & Ethics

Yonah Walker, Yonah Fund
Session Type Icon

Dataset design for technically and legally clean ML development

Michael Osterrieder, vAIsual Inc
Session Type Icon

From Paper to Product – How we implemented BERT

Christoph Henkelmann, DIVISIO GmbH
Session Type Icon

AMA (Ask Me Anything) Session

Dr. Pieter Buteneers, Sinch
Christoph Henkelmann, DIVISIO GmbH
Sebastian Meyen, Software & Support Media
Session Type Icon

3D Human Pose Estimation: How To Use It in AI Coach Apps

Maksym Tatariants, MobiDev
Session Type Icon

Nature inspired optimization algorithms / Algorithm-based Staffing with Ant Colony Optimization

Laura Kölbl, MATHEMA GmbH
Session Type Icon

Increasing Business Efficiency through NLP Integration: Text Summarization and Sentiment Analysis

Anastasiia Vynychenko, MobiDev
Session Type Icon

Speeding up Last-Mile Delivery with AI

Samar Singla, Jungleworks
Session Type Icon

The Future of People

Jeff Blankenburg, Amazon Alexa
Session Type Icon

Affective Computing – bringing humans and machines closer through emotions

Håkan Silfvernagel, Miles Oslo AS
Session Type Icon

Voice assistants with the Fraunhofer SPEAKER project

Jochen Emig, ONSEI GmbH
Leonard Füchsel, ONSEI GmbH
Session Type Icon

No prejudice. No remorse. Unsupervised ML

Liudmyla Taranenko, MobiDev
Session Type Icon

Voice Development with modern AI Tools

Jochen Emig, ONSEI GmbH
Session Type Icon

The Rise of Voice Commerce in India

Kumar Rangarajan, Slang Labs
Session Type Icon

Data as a Product: How To Develop Good Data Products

Bernd Fondermann, bernd fondermann brainlounge
Session Type Icon

Thinking outside the box: Insights From the Visually-Impaired Community into Audio Games Development

Jo Jaquinta, TsaTsaTzu
Session Type Icon

What about the Kids? How Speech Technology transforms Digital Experiences for Kids

Dr. Patricia Scanlon, SoapBox Labs
Session Type Icon

How Voice Search is Evolving: Using Insights from Alexa Voice Search

John Campbell, Rabbit & Pork
Session Type Icon

Creating a testing workbench for ML teams using just Python

Darshita Chaturvedi, Atri Labs
Session Type Icon

Machine Learning outcome in Health Sector in Bangladesh

Dr. Zoha Rahman, Centre For Big Data & Machine Learning
Session Type Icon

Demystifying Data Science

Grishma Jena, IBM
Session Type Icon

How to write a good Training Script?

Christoph Henkelmann, DIVISIO GmbH
Session Type Icon

Brewing Beer with the Help of IoT: Building a DIY Fermentation Sensor

Sven Haiges, SAP Hybris
Session Type Icon

Leveraging Earth Observation Satellite images for Financial Industry

Fekete Csongor, HolistiCRM Kft.
Session Type Icon

Holistic Voice Application Monitoring: How to sleep well every night

Dominik Meißner, 169 Labs GmbH
Session Type Icon

IoT and eSports – Learning for the industry in your spare time

Dr. Tobias Brambach, Point 8 GmbH
Session Type Icon

Let’s learn R by debugging the Currywurst!

Mihailo Joksimovic, Microsoft
Session Type Icon

How to use Natural Language Processing for automatic test case design?

Jannik Fischbach, Qualicen Gmbh
Session Type Icon

So, what exactly is MLOps?

Mihailo Joksimovic, Microsoft
Session Type Icon

Welcome & Introduction

Sebastian Meyen, Software & Support Media
Session Type Icon

Voice Assistants for Customer Support based on Deep Learning

Thomas Abend, VITAS.ai
Session Type Icon

Delivering Rocketfuel – Pipelines für Deep Learning mit Small Data

Hauke Brammer, DeepUp GmbH
Session Type Icon

How to Define and Execute a Successful Data & AI Strategy

Karthik Bharadwaj, H2O AI
Session Type Icon

Data science without data: the cold start playbook

Alex Honchar, Neurons Lab
Session Type Icon

Predictive Maintenance – How Does Data Science Revolutionize the World of Machines?

Victoriya Kalmanovich, Navy
Session Type Icon

Bots testing Bots: From manual to automated testing for conversational AI

Christoph Börner, Botium GmbH
Session Type Icon

Deep Learning: the final Frontier for Time Series Analysis?

Oleksandr Honchar, Neurons Lab / Mawi Solutions
Session Type Icon

AI-powered content moderation

Stefano Tempesta, Microsoft
Session Type Icon

An Introduction to Transformers

Christoph Henkelmann, DIVISIO GmbH
Session Type Icon

Machine learning in the browser with TensorFlow.js

Håkan Silfvernagel, Miles Oslo AS
Session Type Icon

MLOps – from ML model to cloud-service: challenges and pitfalls of scalable document processing

Ward Van Laer, Ixor
Session Type Icon

Kubeflow on a Bare-Metal GPU Cluster from Scratch

Markovtsev Vadim, Athenian
Session Type Icon

Keyword Search is Dead! And so are Solr and Elasticsearch?

Daniel Wrigley, SHI GmbH
Session Type Icon

Architectures that power Machine Learning

Bence Faludi, dataleio
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NLP for career progression: connecting people with jobs and courses

Gabrielle Fournet, Boostrs
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Natural Language Processing (NLP): The Transformer Zoo

Dr. Christian Winkler, datanizing GmbH
Oliver Zeigermann, OPEN KNOWLEDGE GmbH
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Production nightmare of building AI systems at scale – The last mile

Dr. Danish Rafique, Bayer AG
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Evaluating machine learning solutions for subjective problems

Robbe Sneyders, ML6
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Keep an eye on AI — Monitor ML models in production

Hauke Brammer, DeepUp GmbH
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Advances in Machine Learning Systems – from GPUs to Quantum

Andreas Bühlmeier, DBC Enterprice Intelligence
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Self-supervised Learning: Teaching AI common sense

Dr. Pieter Buteneers, Sinch
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Polars: Blazingly fast DataFrames

Ritchie Vink, Xomnia BV
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Human and Multi-agent systems

Dr. Neda Navidi, ETS
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Natural language processing for medical data

Anja Pilz, damedic
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Greenfield and Brownfield Data Labeling – from Idea to Production with data-centric AI

Johannes Hötter, onetask.ai // kern.ai
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AI-powered learning recommendation system: a map and match approach

Khieu Nguyen, Visier Inc.
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AIoT – Die Verbindung zwischen AI und IoT beherrschen!

David Monzel, mm1
Dr. Dirk Slama, Bosch Software Innovations
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How AutoML can give companies, with few data scientists, a key advantage

Ioannis Tsamardinos, JADBio
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Robo programmers? Competent minions? Or just sensational demos and hype?

Mohamed Sayed, Heuro Labs GmbH
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Optimize your network inference time as a data scientist, not as a web developer

Adrian Boguszewski, Intel
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Image Recognition: Past, Present and Future

Oliver Zeigermann, OPEN KNOWLEDGE GmbH
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Practical ML with Power BI Desktop

Markus Ehrenmüller-Jensen, Savory Data
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Underspecified pipelines: Why good models underperform in production

Nikolay Manchev, Domino Data Lab
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IoT Analytics at Scale – Customer based product development

Danilo Dresen, taod Consulting GmbH
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Understanding Artificial Intelligence: Explainable AI with Interpretable KPI Labels

Dr. Rudolf Felix, PSI FLS Fuzzy Logik & Neuro Systeme GmbH
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AI with a devops mindset – experimentation, sharing and easier ML deployment

Thiago de Faria, LINKIT
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MLOps, Automated Machine Learning Made Easy

Jan Mulkens, Ahead with Data
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Machine Learning: The Bare Math Behind Libraries

Piotr Czajka, TomTom
Łukasz Gebel, TomTom
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On-device machine learning – Core ML vs Create ML

Radu Dan, Deloitte Digital Romania
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How We Used Machine Learning to Detect Stress among our Berlin Colleagues

Matthias Hofmaier, Novatec Consulting GmbH
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AI is eating Software – how Second Generation AutoML will replace Software Development

Prof. Adam Blum, Auger.AI
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Building smarter solutions with no expertise in machine learning

Laurent Picard, Google
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AI Red Teaming: a facial recognition case study

Alex Polyakov, Adversa.AI
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AutoML, the Good, the Bad and the Why

Jan Mulkens, Ahead with Data
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Data Analytics Platform

Sameer Paradkar, AtoS
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Real Machine Learning challenges that you’ve never faced at Kaggle

Vladimir Rybakov, WaveAccess
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Interpretable Machine Learning – Application to Finance and Banks

Prof. Debjyoti Paul, Amazon
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Predicting Hotel Cancellations With Machine Learning: Classification and Time Series Analysis

Michael Grogan, MGCodesandStats
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Developing a Match-Making Algorithm Between Customers and Go-Jek Products!

Gunjan Dewan, Go-Jek
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Real, Fake and Deepfake: How Deep Learning Messed up Customer (Re)Identification and How It Will Fix It

Dr. Luca Marchesotti, Sparkd
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Using A.I to make recommendations for career progression

Dorra Nouira, Boostrs
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Automatic Image Cropping for Online Classifieds: a Journey from a Master Thesis to Production

Alexey Grigorev, OLX Group
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Protecting AI Solutions From Attacks

Alex Polyakov, Adversa.AI
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Building Human-in-the-Loop Machine Learning Systems – Fashion 2.0

Steven Bourke, Outfittery
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Next generation AI: Emotional Artificial Intelligence based on audio

Dagmar Schuller, audEERING GmbH
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How to raise intelligent Chatbots, keep them smart and make them useful

Hans-Peter Kuessner, adesso AG
Jens Polster, adesso AG
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MediaPipe: Building real-time cross-platform (mobile, web, edge, desktop) Video Audio ML Pipelines

Ming Yong, Goolge Inc
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The unreasonable effectiveness of simple models

Julien Schuermans, ML6
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The importance and pitfalls of pseudonymization

Lisa Becker, ML6
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Building AI startups with minimum risks

Liubov Zatolokina, MobiDev
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Multi Tasking Deep Learning for Natural Language Processing – Transfer Learning

Prof. Debjyoti Paul, Amazon
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Transforming Business with AI – From Vision to Strategy to Execution

Dr. Tatyana Goldberg, Allianz
Dr. Guy Yachdav, Aiola
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Battle of the Languages: Java and Python compete to solve programming challenges

Michael Inden, Freelancer
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From 2D to 3D: Unsupervised Reconstruction in Computer Vision using U-CMR

Thomas Chen, Academy for Mathematics, Science, and Engineering
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Honey Bee Conservation using Deep Learning

Thiago da Silva Alves, JArchitects
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Honey, I shrunk the TinyML

Lars Gregori, SAP CX
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#An Introduction to Clustering Algorithms

Christoph Henkelmann, DIVISIO GmbH
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Smaller, Faster, Cheaper: CNN Optimisation Techniques in Object Detection

Ilia Lorin, MobiDev
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Let’s talk about Recommendation Systems!

Mihailo Joksimovic, Microsoft
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Preserve data privacy in Machine Learning with Confidential Computing

Stefano Tempesta, Microsoft
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The Phoenix Project, how simulations can create high performing teams

Jan Schilt, GamingWorks
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Real time Covid-19 detection with Audio AI

Pia Eggert, Freelancer
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Unsupervised Learning with Autoencoders

Christoph Henkelmann, DIVISIO GmbH
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Artificial Intelligence and Machine Learning

Surendran Selvaraju, Schlumberger
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A Few Million Words Later – Text Analytics for Software Quality Assurance in Practice

Dr. Maximilian Junker, Qualicen
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Coded Leadership: Developing Scalable Management in an AI-induced Quantum World

Dr. Raul Rodriguez, Woxsen University
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Warning: this is not another MLOps best practices talk

Amit Bendor, Artlist
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Machine Learning Techniques for Estimating Science and Properties of Materials

Dr. Beauty Pandey, WOXSEN University Hyderabad
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Introduction to Sentence Embeddings

Dr. Christian Winkler, datanizing GmbH
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Designing Conversational Chatbot for Digital Operators

Burak Aytan, TURKCELL
Dr. Ilknur Durgar, TURKCELL
Muhammed Güre, TURKCELL
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Vector embedding AI in practice

Jon Bratseth, Yahoo
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NLU in Other Languages

Burak Isikli, Akbank
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Satellite imagery analysis: Machine Learning to prevent environmental crisis

Vladimir Rybakov, WaveAccess
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AbadIA: How to build an AI from zero to learn to play and solve a tough game

Juantomás Garcia, Sngular People S.L
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An Introduction to Natural Language Generation

Christoph Henkelmann, DIVISIO GmbH
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Fast and accurate: AI-based video processing

Sergey Maximenko, MobiDev

Sessions Berlin 2022

Tue

29. Nov 2022
11:15 - 12:00
Salon 3
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Software 2.0: How does data become an AI software artifact?

Leonard Püttmann, onetask.ai // kern.ai
11:15 - 12:00
Salon 4
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How AutoML can give companies, with few data scientists, a key advantage

Ioannis Tsamardinos, JADBio
12:30 - 13:15
Salon 3
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Data Centric AI vs Model Centric AI – The “right” data vs “best” model culture

Prof. Debjyoti Paul, Amazon
12:30 - 13:15
Salon 4
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Holistic Voice Application Monitoring: How to sleep well every night

Dominik Meißner, 169 Labs GmbH
14:15 - 15:00
Salon 4
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Let’s learn R by debugging the Currywurst!

Mihailo Joksimovic, Microsoft
14:15 - 15:00
Salon 3
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MLOps – from ML model to cloud-service: challenges and pitfalls of scalable document processing

Ward Van Laer, Ixor
15:30 - 16:15
Salon 4
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Vector embedding AI in production systems

Jon Bratseth, Yahoo
15:30 - 16:15
Salon 3
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Data as a Product: How To Develop Good Data Products

Bernd Fondermann, bernd fondermann brainlounge
16:45 - 17:30
Salon 4
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Leveraging Earth Observation Satellite images for Financial Industry

Fekete Csongor, HolistiCRM Kft.
16:45 - 17:30
Salon 3
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AI-powered learning recommendation system: a map and match approach

Khieu Nguyen, Visier Inc.

Wed

30. Nov 2022
09:00 - 09:45
Salon 3
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Warning: this is not another MLOps best practices talk

Amit Bendor, Artlist
09:00 - 09:45
Salon 4
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Machine Learning Techniques for Estimating Science and Properties of Materials

Dr. Beauty Pandey, WOXSEN University Hyderabad
10:15 - 11:00
Salon 4
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Image Recognition: Past, Present and Future

Oliver Zeigermann, OPEN KNOWLEDGE GmbH
10:15 - 11:00
Salon 3
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The importance and pitfalls of pseudonymization

Lisa Becker, ML6
11:30 - 12:30
Salon 4
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AMA (Ask Me Anything) Session

Dr. Pieter Buteneers, Sinch
Christoph Henkelmann, DIVISIO GmbH
Sebastian Meyen, Software & Support Media
13:30 - 14:15
Salon 3
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Practical Experiences from setting up the complete MLOps pipeline

Prof. Dr. René Brunner, Datamics / Professor an der Hochschule Macromedia
Eric Joachim Liese, BSH Home Appliances Group
13:30 - 14:15
Salon 4
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So, what exactly is MLOps?

Mihailo Joksimovic, Microsoft
14:45 - 15:30
Salon 3
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Dataset design for technically and legally clean ML development

Michael Osterrieder, vAIsual Inc
14:45 - 15:30
Salon 4
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How to write a good Training Script?

Christoph Henkelmann, DIVISIO GmbH
16:00 - 16:45
Salon 4
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Creating a testing workbench for ML teams using just Python

Darshita Chaturvedi, Atri Labs
16:00 - 16:45
Salon 3
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Data science without data: the cold start playbook

Alex Honchar, Neurons Lab

Sessions Singapore 2022

Wed

23. Nov 2022
11:15 - 12:00
Stage 1
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Preserve data privacy in Machine Learning with Confidential Computing

Stefano Tempesta, Microsoft
11:15 - 12:00
Stage 2 - SNG
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Coded Leadership: Developing Scalable Management in an AI-induced Quantum World

Dr. Raul Rodriguez, Woxsen University
13:00 - 13:45
Stage 1
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AI-powered content moderation

Stefano Tempesta, Microsoft
13:00 - 13:45
Stage 2 - SNG
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Data Analytics Platform

Sameer Paradkar, AtoS
14:00 - 14:45
Stage 1
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Warning: this is not another MLOps best practices talk

Amit Bendor, Artlist
15:15 - 16:00
Stage 2 - SNG
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The importance and pitfalls of pseudonymization

Lisa Becker, ML6
15:15 - 16:00
Stage 1
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Let’s talk about Recommendation Systems!

Mihailo Joksimovic, Microsoft
16:15 - 17:00
Stage 1
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How are AI and ML helping to boost our agile development process

Dr. Rachid Kherrazi, AKKA Netherlands B.V.
17:30 - 18:15
Stage 1
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Machine Learning Sucks

Dr. Pieter Buteneers, Sinch

Thu

24. Nov 2022
11:15 - 12:00
Stage 1 - SNG
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Artificial Intelligence and Machine Learning

Surendran Selvaraju, Schlumberger
11:15 - 12:00
Stage 2
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Machine Learning Techniques for Estimating Science and Properties of Materials

Dr. Beauty Pandey, WOXSEN University Hyderabad
13:00 - 13:45
Stage 2 - SNG
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Data Centric AI vs Model Centric AI – The “right” data vs “best” model culture

Prof. Debjyoti Paul, Amazon
13:00 - 13:45
Stage 1
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How to Define and Execute a Successful Data & AI Strategy

Karthik Bharadwaj, H2O AI
14:00 - 14:45
Stage 1
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How We Covered Concept Drifts In Time Series Forecasting For Public Transport

Dr. Tim Frey, iunera GmbH & Co. KG
14:00 - 14:45
Stage 2
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How AutoML can give companies, with few data scientists, a key advantage

Ioannis Tsamardinos, JADBio
15:15 - 16:00
Stage 2
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Machine learning in the browser with TensorFlow.js

Håkan Silfvernagel, Miles Oslo AS
16:15 - 17:00
Stage 2
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Machine Learning outcome in Health Sector in Bangladesh

Dr. Zoha Rahman, Centre For Big Data & Machine Learning
16:15 - 17:00
Stage 1
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#An Introduction to Clustering Algorithms

Christoph Henkelmann, DIVISIO GmbH
17:30 - 18:15
Stage 1
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Affective Computing – bringing humans and machines closer through emotions

Håkan Silfvernagel, Miles Oslo AS

Sessions Munich 2022

Tue

28. Jun 2022
10:15 - 11:00
Kopernikus 2
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Keep an eye on AI — Monitor ML models in production

Hauke Brammer, DeepUp GmbH
10:15 - 11:00
Kopernikus 3
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How AutoML can give companies, with few data scientists, a key advantage

Ioannis Tsamardinos, JADBio
11:30 - 12:15
Kopernikus 3
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Self-supervised Learning: Teaching AI common sense

Dr. Pieter Buteneers, Sinch
11:30 - 12:15
Kopernikus 1
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Greenfield and Brownfield Data Labeling – from Idea to Production with data-centric AI

Johannes Hötter, onetask.ai // kern.ai
11:30 - 12:15
Kopernikus 2
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Underspecified pipelines: Why good models underperform in production

Nikolay Manchev, Domino Data Lab
14:30 - 15:15
Kopernikus 3
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Threat modeling and security assessment for ML models

Alex Polyakov, Adversa.AI
14:30 - 15:15
Kopernikus 1
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Nature inspired optimization algorithms / Algorithm-based Staffing with Ant Colony Optimization

Laura Kölbl, MATHEMA GmbH
14:30 - 15:15
Kopernikus 2
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Using A.I to make recommendations for career progression

Dorra Nouira, Boostrs
15:45 - 16:30
Kopernikus 2
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Kotlin? For Machine Learning?

Hauke Brammer, DeepUp GmbH
15:45 - 16:30
Kopernikus 3
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Introduction to Sentence Embeddings

Dr. Christian Winkler, datanizing GmbH
17:30 - 18:15
Kopernikus 2
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Honey, I shrunk the TinyML

Lars Gregori, SAP CX
17:30 - 18:15
Kopernikus 3
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Warning: this is not another MLOps best practices talk

Amit Bendor, Artlist

Wed

29. Jun 2022
10:15 - 11:00
Kopernikus 3
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How Machine Learning is (becoming) creative

Andreas Bühlmeier, DBC Enterprice Intelligence
10:15 - 11:00
Kopernikus 2
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Machine Learning on the Edge using TensorFlow Lite

Håkan Silfvernagel, Miles Oslo AS
10:15 - 11:00
Kopernikus 1
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Optimize your network inference time as a data scientist, not as a web developer

Adrian Boguszewski, Intel
10:15 - 11:00
Einstein 1
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IoT Analytics at Scale – Customer based product development

Danilo Dresen, taod Consulting GmbH
11:30 - 12:15
Kopernikus 2
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Why Security is Important in ML and How to Secure your ML-based Solutions

Dr. Rachid Kherrazi, AKKA Netherlands B.V.
11:30 - 12:15
Kopernikus 3
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The unreasonable effectiveness of simple models

Julien Schuermans, ML6
14:45 - 15:30
Kopernikus 2
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Vector embedding AI in production systems

Jon Bratseth, Yahoo
14:45 - 15:30
Kopernikus 3
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The importance and pitfalls of pseudonymization

Lisa Becker, ML6
16:00 - 16:45
Kopernikus 3
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k-NN on steroids – an introduction to approximate nearest neighbours

Kacper Łukawski, Qdrant
16:00 - 16:45
Einstein 1
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State-of-the-art in large scale recommender systems

Aditya Gulgani, Meta
17:15 - 18:00
Kopernikus 2
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How We Covered Concept Drifts In Time Series Forecasting For Public Transport

Dr. Tim Frey, iunera GmbH & Co. KG
17:15 - 18:00
Kopernikus 3
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Dataset design for technically and legally clean ML development

Michael Osterrieder, vAIsual Inc