Sessions

ML Conference
The Conference for Machine Learning Innovation

Sessions

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Machine Learning in a Browser

Athan Reines, Quansight
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Future of HR: Implementation of RPA and AI in HR Processes Using Python and TensorFlow

Prof. Raul Rodríguez, Woxsen School of Business
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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
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Scaling Machine Learning Systems up to Billions of Predictions per Day

Carmine Paolino, OLX Group
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Applying Machine Learning Online at Scale

Jon Bratseth, Verizon
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Towards a Convenient ML Infra

Vadim Markovtsev, source{d}
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NLU in Other Languages

Burak Isikli, Turkcell
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Understanding how Neural Networks work by looking at the Low Level API of TensorFlow 2

Oliver Zeigermann, embarc
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Asynchronous and version-controlled data science with Jupyter notebooks

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

Jens Beyer, LAVRIO.solutions 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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Continuous Delivery for Machine Learning Applications with Open Source Tools

Daniel Schruhl, ThoughtWorks Germany
Christoph Windheuser, ThoughtWorks Germany
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Privacy-preserving Machine Learning

Nicolas Kuhaupt, Fraunhofer IEE
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Client-side Artificial Intelligence in JavaScript using Tensorflow.js

Mathias Burger, TNG Technology GmbH
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All You Need to Know now about (Deep) Reinforcement Learning

Dr. Andreas Bühlmeier, DBC Enterprise IT Consulting
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Case Study: Making the Most out of a Doomed Project

Vladimir Rybakov, WaveAccess
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From Paper to Product – How we implemented BERT

Christoph Henkelmann, DIVISIO
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Speeding up Last-Mile Delivery with AI

Samar Singla, Jungleworks
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Market Segmentation in the Era of Big Data

Özge Sahin, SciScry
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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, Novatec Consulting GmbH
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Predictive Maintenance – How Does Data Science Revolutionize the World of Machines?

Victoriya Kalmanovich, Navy
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Deep Learning: the final Frontier for Time Series Analysis?

Oleksandr Honchar, Neurons Lab / Mawi Solutions
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Architectures that power Machine Learning

Bence Faludi, dataleio
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Production nightmare of building AI systems at scale – The last mile

Dr. Danish Rafique, TerraLoupe GmbH
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Human / AI Interaction Loop Training as a New Approach for Interactive Learning With Reinforcement-Learning Agents

Dr. Neda Navidi, AIR (AI Redefined)
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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 for Decision Makers

Uri Eliabayev, Machine & Deep Learning Israel
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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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AutoML, the Good, the Bad and the Why

Jan Mulkens, Ahead with Data
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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, Beautifeye - Spaces
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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

Alexander Polyakov, Independent Researcher
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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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An Introduction to Natural Language Generation

Christoph Henkelmann, DIVISIO
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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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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. Guy Yachdav, Artiio
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Honey Bee Conservation using Deep Learning

Thiago da Silva Alves, JArchitects

All Sessions

Session Type Icon

Machine Learning in a Browser

Athan Reines, Quansight
Session Type Icon

Future of HR: Implementation of RPA and AI in HR Processes Using Python and TensorFlow

Prof. Raul Rodríguez, Woxsen School of Business
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
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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
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Scaling Machine Learning Systems up to Billions of Predictions per Day

Carmine Paolino, OLX Group
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Applying Machine Learning Online at Scale

Jon Bratseth, Verizon
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Towards a Convenient ML Infra

Vadim Markovtsev, source{d}
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NLU in Other Languages

Burak Isikli, Turkcell
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Understanding how Neural Networks work by looking at the Low Level API of TensorFlow 2

Oliver Zeigermann, embarc
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Asynchronous and version-controlled data science with Jupyter notebooks

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

Jens Beyer, LAVRIO.solutions 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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Continuous Delivery for Machine Learning Applications with Open Source Tools

Daniel Schruhl, ThoughtWorks Germany
Christoph Windheuser, ThoughtWorks Germany
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Privacy-preserving Machine Learning

Nicolas Kuhaupt, Fraunhofer IEE
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Client-side Artificial Intelligence in JavaScript using Tensorflow.js

Mathias Burger, TNG Technology GmbH
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All You Need to Know now about (Deep) Reinforcement Learning

Dr. Andreas Bühlmeier, DBC Enterprise IT Consulting
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Case Study: Making the Most out of a Doomed Project

Vladimir Rybakov, WaveAccess
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From Paper to Product – How we implemented BERT

Christoph Henkelmann, DIVISIO
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Speeding up Last-Mile Delivery with AI

Samar Singla, Jungleworks
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Market Segmentation in the Era of Big Data

Özge Sahin, SciScry
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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, Novatec Consulting GmbH
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Predictive Maintenance – How Does Data Science Revolutionize the World of Machines?

Victoriya Kalmanovich, Navy
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Deep Learning: the final Frontier for Time Series Analysis?

Oleksandr Honchar, Neurons Lab / Mawi Solutions
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Architectures that power Machine Learning

Bence Faludi, dataleio
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Production nightmare of building AI systems at scale – The last mile

Dr. Danish Rafique, TerraLoupe GmbH
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Human / AI Interaction Loop Training as a New Approach for Interactive Learning With Reinforcement-Learning Agents

Dr. Neda Navidi, AIR (AI Redefined)
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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 for Decision Makers

Uri Eliabayev, Machine & Deep Learning Israel
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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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AutoML, the Good, the Bad and the Why

Jan Mulkens, Ahead with Data
Session Type Icon

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
Session Type Icon

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, Beautifeye - Spaces
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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

Alexander Polyakov, Independent Researcher
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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
Session Type Icon

An Introduction to Natural Language Generation

Christoph Henkelmann, DIVISIO
Session Type Icon

How to raise intelligent Chatbots, keep them smart and make them useful

Hans-Peter Kuessner, adesso AG
Jens Polster, adesso AG
Session Type Icon

MediaPipe: Building real-time cross-platform (mobile, web, edge, desktop) Video Audio ML Pipelines

Ming Yong, Goolge Inc
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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. Guy Yachdav, Artiio
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Honey Bee Conservation using Deep Learning

Thiago da Silva Alves, JArchitects

Sessions Berlin 2019

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Human / AI Interaction Loop Training as a New Approach for Interactive Learning With Reinforcement-Learning Agents

Dr. Neda Navidi, AIR (AI Redefined)

Tue

10. Dec 2019
10:15 - 11:00
Salon 1
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Continuous Delivery for Machine Learning Applications with Open Source Tools

Daniel Schruhl, ThoughtWorks Germany
Christoph Windheuser, ThoughtWorks Germany
10:15 - 11:00
Saal A+B
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Deep Learning: the final Frontier for Time Series Analysis?

Oleksandr Honchar, Neurons Lab / Mawi Solutions
10:15 - 11:00
Salon 8+9
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Honey Bee Conservation using Deep Learning

Thiago da Silva Alves, JArchitects
11:15 - 12:00
Saal A+B
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Privacy-preserving Machine Learning

Nicolas Kuhaupt, Fraunhofer IEE
11:15 - 12:00
Salon 8+9
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Market Segmentation in the Era of Big Data

Özge Sahin, SciScry
14:15 - 15:00
Saal A+B
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Understanding how Neural Networks work by looking at the Low Level API of TensorFlow 2

Oliver Zeigermann, embarc
14:15 - 15:00
Salon 8+9
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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
15:30 - 16:15
Salon 8+9
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How We Used Machine Learning to Detect Stress among our Berlin Colleagues

Matthias Hofmaier, Novatec Consulting GmbH
15:30 - 16:15
Salon 1
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Building Human-in-the-Loop Machine Learning Systems – Fashion 2.0

Steven Bourke, Outfittery
16:30 - 17:15
Saal A+B
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Next generation AI: Emotional Artificial Intelligence based on audio

Dagmar Schuller, audEERING GmbH

Wed

11. Dec 2019
09:00 - 09:45
Salon 1
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Asynchronous and version-controlled data science with Jupyter notebooks

Juha Kiili, Valohai
09:00 - 09:45
Saal A+B
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AI is eating Software – how Second Generation AutoML will replace Software Development

Prof. Adam Blum, Auger.AI
10:15 - 11:00
Saal A+B
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Case Study: Making the Most out of a Doomed Project

Vladimir Rybakov, WaveAccess
10:15 - 11:00
Salon 6+7
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Voice Assistants for Customer Support based on Deep Learning

Thomas Abend, VITAS.ai
10:15 - 11:00
Salon 8+9
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MediaPipe: Building real-time cross-platform (mobile, web, edge, desktop) Video Audio ML Pipelines

Ming Yong, Goolge Inc
11:15 - 12:00
Saal A+B
Session Type Icon

Client-side Artificial Intelligence in JavaScript using Tensorflow.js

Mathias Burger, TNG Technology GmbH
11:15 - 12:00
Salon 8+9
Session Type Icon

Automatic Image Cropping for Online Classifieds: a Journey from a Master Thesis to Production

Alexey Grigorev, OLX Group
14:15 - 15:00
Saal A+B
Session Type Icon

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

Jens Beyer, LAVRIO.solutions GmbH
14:15 - 15:00
Salon 8+9
Session Type Icon

From Paper to Product – How we implemented BERT

Christoph Henkelmann, DIVISIO
14:15 - 15:00
Salon 1
Session Type Icon

Delivering Rocketfuel – Pipelines für Deep Learning mit Small Data

Hauke Brammer, Novatec Consulting GmbH
15:30 - 16:15
Saal A+B
Session Type Icon

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

Dr. Andreas Bühlmeier, DBC Enterprise IT Consulting
15:30 - 16:15
Salon 8+9
Session Type Icon

Speeding up Last-Mile Delivery with AI

Samar Singla, Jungleworks

Sessions Singapore 2020

Session Type Icon

Machine Learning in a Browser

Athan Reines, Quansight
Session Type Icon

Future of HR: Implementation of RPA and AI in HR Processes Using Python and TensorFlow

Prof. Raul Rodríguez, Woxsen School of Business
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
Session Type Icon

Scaling Machine Learning Systems up to Billions of Predictions per Day

Carmine Paolino, OLX Group
Session Type Icon

Applying Machine Learning Online at Scale

Jon Bratseth, Verizon
Session Type Icon

Towards a Convenient ML Infra

Vadim Markovtsev, source{d}
Session Type Icon

NLU in Other Languages

Burak Isikli, Turkcell
Session Type Icon

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

Jesus Rodriguez, Invector Labs
Session Type Icon

Speeding up Last-Mile Delivery with AI

Samar Singla, Jungleworks
Session Type Icon

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

Victoriya Kalmanovich, Navy
Session Type Icon

Architectures that power Machine Learning

Bence Faludi, dataleio
Session Type Icon

Real Machine Learning Challenges That You’ve Never Faced at Kaggle

Vladimir Rybakov, WaveAccess
Session Type Icon

Multi Tasking Deep Learning for Natural Language Processing – Transfer Learning

Prof. Debjyoti Paul, Amazon

Sessions Munich 2020

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Scaling Machine Learning Systems up to Billions of Predictions per Day

Carmine Paolino, OLX Group
Session Type Icon

Applying Machine Learning Online at Scale

Jon Bratseth, Verizon
Session Type Icon

NLU in Other Languages

Burak Isikli, Turkcell
Session Type Icon

Production nightmare of building AI systems at scale – The last mile

Dr. Danish Rafique, TerraLoupe GmbH
Session Type Icon

Human / AI Interaction Loop Training as a New Approach for Interactive Learning With Reinforcement-Learning Agents

Dr. Neda Navidi, AIR (AI Redefined)
Session Type Icon

AI for Decision Makers

Uri Eliabayev, Machine & Deep Learning Israel
Session Type Icon

Building Smarter Solutions With No Expertise in Machine Learning

Laurent Picard, Google
Session Type Icon

AutoML, the Good, the Bad and the Why

Jan Mulkens, Ahead with Data
Session Type Icon

Interpretable Machine Learning – Application to Finance and Banks

Prof. Debjyoti Paul, Amazon
Session Type Icon

Predicting Hotel Cancellations With Machine Learning: Classification and Time Series Analysis

Michael Grogan, MGCodesandStats
Session Type Icon

Developing a Match-Making Algorithm Between Customers and Go-Jek Products!

Gunjan Dewan, Go-Jek
Session Type Icon

Real, Fake and Deepfake: How Deep Learning Messed up Customer (Re)Identification and How It Will Fix It

Dr. Luca Marchesotti, Beautifeye - Spaces
Session Type Icon

Protecting AI Solutions From Attacks

Alexander Polyakov, Independent Researcher
Session Type Icon

An Introduction to Natural Language Generation

Christoph Henkelmann, DIVISIO
Session Type Icon

Transforming business with AI – from vision to strategy to execution

Dr. Guy Yachdav, Artiio