ONLY UNTIL APRIL 20: ✓ SAVE UP TO 440€ ✓ GADGET FOR FREE ✓ FULLSTACK SUBSCRIPTION

ONLY UNTIL APRIL 20: ✓ SAVE UP TO 440€ ✓ GADGET FOR FREE ✓ FULLSTACK SUBSCRIPTION

ONLY UNTIL JUNE 15: ✓ SAVE UP TO $1002 ✓ WORKSHOP DAY FOR FREE ✓ TEAM DISCOUNT

ONLY UNTIL JUNE 15: ✓ SAVE UP TO $1002 ✓ WORKSHOP DAY FOR FREE ✓ TEAM DISCOUNT

Sessions

MLCON 2023
The Event for Machine Learning Technologies & Innovations
June 19 - 22, 2023 | Munich or online

All Sessions

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

Kacper Łukawski, Qdrant
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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
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Software 2.0: How does data become an AI software artifact?

Leonard Püttmann, onetask.ai // kern.ai
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Have you really embraced audio?

Benjamin McCulloch, Conversation Designer
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Future of HR from 2021: Reimagining the Future.

Future of HR from 2021: Reimagining the Future.,
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Threat modeling and security assessment for ML models

Threat modeling and security assessment for ML models,
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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
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Playing Doom with TF-Agents and TensorFlow 2.0

Andreas Eberle, arconsis IT-Solutions GmbH
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Using Neural Networks for Natural Language Processing

Christoph Henkelmann, DIVISIO GmbH
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Scaling Machine Learning Systems up to Billions of Predictions per Day

Carmine Paolino, Alan Turing Institute
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Applying machine learning online at scale

Jon Bratseth, Yahoo
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Towards a Convenient ML Infra

Vadim Markovtsev, source{d}
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Understanding how Neural Networks work by looking at the Low Level API of TensorFlow 2

Oliver Zeigermann, OPEN KNOWLEDGE GmbH
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Streamlining your Machine Learning Projects using MLOps with Azure Machine Learning

Sam Vanhoutte, Codit
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Stopping Public Transport Coronavirus Infections with Big Data

Dr. Tim Frey, iunera GmbH & Co. KG
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Asynchronous and version-controlled data science with Jupyter notebooks

Juha Kiili, Valohai
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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.
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Dockerize, Standardize, Deploy and Scale your own Machine Learning Model

Jens Beyer, LAVRIO.solutions GmbH
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Leveraging Reinforcement Learning for Improvement of Traditional Controllers for a Vehicle Model – A Case Study

Christoph Stockhammer, The MathWorks GmbH
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Some Things I Wish I Had Known about Scaling Machine Learning Solutions

Jesus Rodriguez, Invector Labs
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Machine Learning on the Edge using TensorFlow Lite

Håkan Silfvernagel, Miles Oslo AS
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Continuous Delivery for Machine Learning Applications with Open Source Tools

Daniel Schruhl, ThoughtWorks Germany
Christoph Windheuser, Databricks
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Pre-Moderation – The Safe Sandbox for your Community

Christian Dedié, gutefrage.net GmbH
Dr. Katsiaryna Stalpouskaya, gutefrage.net GmbH
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Vector embedding AI in production systems

Jon Bratseth, Yahoo
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Privacy-preserving Machine Learning

Nicolas Kuhaupt, Fraunhofer IEE
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Efficient Transformers

Christoph Henkelmann, DIVISIO GmbH
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Client-side Artificial Intelligence in JavaScript using Tensorflow.js

Mathias Burger, TNG Technology GmbH
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Enhancing Page Visits by Topic Prediction

Dieter Jordens, Dieter Jordens IT Consulting BV
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How We Covered Concept Drifts In Time Series Forecasting For Public Transport

Dr. Tim Frey, iunera GmbH & Co. KG
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The Bigger the Better for NLP – or not?

Dr. Maximilian Junker, Qualicen
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Kotlin? For Machine Learning?

Hauke Brammer, DeepUp GmbH
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All You Need to Know now about (Deep) Reinforcement Learning

Andreas Bühlmeier, DBC Enterprice Intelligence
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Lean Data Science: Tailoring Agile Practices for Data Science Projects

Askhat Urazbaev, ScrumTrek
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Unsupervised Category-Specific Mesh Reconstruction: A Future ML Opportunity

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

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

Yonah Walker, Yonah Fund
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Dataset design for technically and legally clean ML development

Michael Osterrieder, vAIsual Inc
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From Paper to Product – How we implemented BERT

Christoph Henkelmann, DIVISIO GmbH
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AMA (Ask Me Anything) Session

AMA (Ask Me Anything) Session,
Christoph Henkelmann, DIVISIO GmbH
Sebastian Meyen, Software & Support Media
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3D Human Pose Estimation: How To Use It in AI Coach Apps

Maksym Tatariants, MobiDev
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Nature inspired optimization algorithms / Algorithm-based Staffing with Ant Colony Optimization

Laura Kölbl, MATHEMA GmbH
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Increasing Business Efficiency through NLP Integration: Text Summarization and Sentiment Analysis

Anastasiia Vynychenko, MobiDev
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Speeding up Last-Mile Delivery with AI

Samar Singla, Jungleworks
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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
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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
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Machine Learning outcome in Health Sector in Bangladesh

Dr. Zoha Rahman, Centre For Big Data & Machine Learning
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Demystifying Data Science

Grishma Jena, IBM
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How to write a good Training Script?

Christoph Henkelmann, DIVISIO GmbH
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Brewing Beer with the Help of IoT: Building a DIY Fermentation Sensor

Sven Haiges, SAP Hybris
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Leveraging Earth Observation Satellite images for Financial Industry

Fekete Csongor, HolistiCRM Kft.
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Holistic Voice Application Monitoring: How to sleep well every night

Holistic Voice Application Monitoring: How to sleep well every night,
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IoT and eSports – Learning for the industry in your spare time

Dr. Tobias Brambach, Point 8 GmbH
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Let’s learn R by debugging the Currywurst!

Mihailo Joksimovic, Microsoft
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How to use Natural Language Processing for automatic test case design?

Jannik Fischbach, Qualicen Gmbh
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So, what exactly is MLOps?

Mihailo Joksimovic, Microsoft
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Welcome & Introduction

Sebastian Meyen, Software & Support Media
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Voice Assistants for Customer Support based on Deep Learning

Thomas Abend, VITAS.ai
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Delivering Rocketfuel – Pipelines für Deep Learning mit Small Data

Hauke Brammer, DeepUp GmbH
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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
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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
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Machine learning in the browser with TensorFlow.js

Håkan Silfvernagel, Miles Oslo AS
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MLOps – from ML model to cloud-service: challenges and pitfalls of scalable document processing

Ward Van Laer, Ixor
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Kubeflow on a Bare-Metal GPU Cluster from Scratch

Markovtsev Vadim, Athenian
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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

Self-supervised Learning: Teaching AI common sense,
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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

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

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

Leonard Püttmann, onetask.ai // kern.ai
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How AutoML can give companies, with few data scientists, a key advantage

Ioannis Tsamardinos, JADBio
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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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Holistic Voice Application Monitoring: How to sleep well every night

Holistic Voice Application Monitoring: How to sleep well every night,
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Let’s learn R by debugging the Currywurst!

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

Ward Van Laer, Ixor
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Vector embedding AI in production systems

Jon Bratseth, Yahoo
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Data as a Product: How To Develop Good Data Products

Bernd Fondermann, bernd fondermann brainlounge
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Leveraging Earth Observation Satellite images for Financial Industry

Fekete Csongor, HolistiCRM Kft.
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AI-powered learning recommendation system: a map and match approach

Khieu Nguyen, Visier Inc.
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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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Image Recognition: Past, Present and Future

Oliver Zeigermann, OPEN KNOWLEDGE GmbH
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The importance and pitfalls of pseudonymization

Lisa Becker, ML6
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AMA (Ask Me Anything) Session

AMA (Ask Me Anything) Session,
Christoph Henkelmann, DIVISIO GmbH
Sebastian Meyen, Software & Support Media
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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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So, what exactly is MLOps?

Mihailo Joksimovic, Microsoft
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Dataset design for technically and legally clean ML development

Michael Osterrieder, vAIsual Inc
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How to write a good Training Script?

Christoph Henkelmann, DIVISIO GmbH
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Creating a testing workbench for ML teams using just Python

Darshita Chaturvedi, Atri Labs
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Data science without data: the cold start playbook

Alex Honchar, Neurons Lab

Sessions Singapore 2022

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Preserve data privacy in Machine Learning with Confidential Computing

Stefano Tempesta, Microsoft
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Coded Leadership: Developing Scalable Management in an AI-induced Quantum World

Dr. Raul Rodriguez, Woxsen University
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AI-powered content moderation

Stefano Tempesta, Microsoft
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Data Analytics Platform

Sameer Paradkar, AtoS
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Warning: this is not another MLOps best practices talk

Amit Bendor, Artlist
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The importance and pitfalls of pseudonymization

Lisa Becker, ML6
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Let’s talk about Recommendation Systems!

Mihailo Joksimovic, Microsoft
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How are AI and ML helping to boost our agile development process

Dr. Rachid Kherrazi, AKKA Netherlands B.V.
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Machine Learning Sucks

Machine Learning Sucks,
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Artificial Intelligence and Machine Learning

Surendran Selvaraju, Schlumberger
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Machine Learning Techniques for Estimating Science and Properties of Materials

Dr. Beauty Pandey, WOXSEN University Hyderabad
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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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How to Define and Execute a Successful Data & AI Strategy

Karthik Bharadwaj, H2O AI
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How We Covered Concept Drifts In Time Series Forecasting For Public Transport

Dr. Tim Frey, iunera GmbH & Co. KG
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How AutoML can give companies, with few data scientists, a key advantage

Ioannis Tsamardinos, JADBio
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Machine learning in the browser with TensorFlow.js

Håkan Silfvernagel, Miles Oslo AS
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Machine Learning outcome in Health Sector in Bangladesh

Dr. Zoha Rahman, Centre For Big Data & Machine Learning
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#An Introduction to Clustering Algorithms

Christoph Henkelmann, DIVISIO GmbH
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Affective Computing – bringing humans and machines closer through emotions

Håkan Silfvernagel, Miles Oslo AS

Sessions Munich 2023