Sessions

ML Conference
The Conference for Machine Learning Innovation

Sessions

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

Alexey Grigorev, OLX Group
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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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Multi Tasking Deep Learning for Natural Language Processing – Transfer Learning

Debjyoti Paul, Amazon
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Honey Bee Conservation using Deep Learning

Thiago da Silva Alves, JArchitects

All Sessions

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

Alexey Grigorev, OLX Group
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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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Multi Tasking Deep Learning for Natural Language Processing – Transfer Learning

Debjyoti Paul, Amazon
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Honey Bee Conservation using Deep Learning

Thiago da Silva Alves, JArchitects

Sessions Berlin 2019

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
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Client-side Artificial Intelligence in JavaScript using Tensorflow.js

Mathias Burger, TNG Technology GmbH
11:15 - 12:00
Salon 8+9
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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
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Dockerize, Standardize, Deploy and Scale your own Machine Learning Model

Jens Beyer, LAVRIO.solutions GmbH
14:15 - 15:00
Salon 8+9
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From Paper to Product – How we implemented BERT

Christoph Henkelmann, DIVISIO
14:15 - 15:00
Salon 1
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Delivering Rocketfuel – Pipelines für Deep Learning mit Small Data

Hauke Brammer, Novatec Consulting GmbH
15:30 - 16:15
Saal A+B
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All You Need to Know now about (Deep) Reinforcement Learning

Dr. Andreas Bühlmeier, DBC Enterprise IT Consulting
15:30 - 16:15
Salon 8+9
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Speeding up last-mile Delivery with AI

Samar Singla, Jungleworks

Sessions Singapore 2020

Sessions Munich 2020

The sessions of ML Conference Munich 2020 will be announced soon! As reference, please see the sessions of the next ML Conference in Berlin below.
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Understanding how Neural Networks work by looking at the Low Level API of TensorFlow 2

Oliver Zeigermann, embarc
Session Type Icon

Asynchronous and version-controlled data science with Jupyter notebooks

Juha Kiili, Valohai
Session Type Icon

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

Jens Beyer, LAVRIO.solutions GmbH
Session Type Icon

Continuous Delivery for Machine Learning Applications with Open Source Tools

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

Privacy-preserving Machine Learning

Nicolas Kuhaupt, Fraunhofer IEE
Session Type Icon

Client-side Artificial Intelligence in JavaScript using Tensorflow.js

Mathias Burger, TNG Technology GmbH
Session Type Icon

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

Dr. Andreas Bühlmeier, DBC Enterprise IT Consulting
Session Type Icon

Case Study: Making the Most out of a Doomed Project

Vladimir Rybakov, WaveAccess
Session Type Icon

From Paper to Product – How we implemented BERT

Christoph Henkelmann, DIVISIO
Session Type Icon

Speeding up last-mile Delivery with AI

Samar Singla, Jungleworks
Session Type Icon

Market Segmentation in the Era of Big Data

Özge Sahin, SciScry
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, Novatec Consulting GmbH
Session Type Icon

Deep Learning: the final Frontier for Time Series Analysis?

Oleksandr Honchar, Neurons Lab / Mawi Solutions
Session Type Icon

How We Used Machine Learning to Detect Stress among our Berlin Colleagues

Matthias Hofmaier, Novatec Consulting GmbH
Session Type Icon

AI is eating Software – how Second Generation AutoML will replace Software Development

Prof. Adam Blum, Auger.AI
Session Type Icon

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

Alexey Grigorev, OLX Group
Session Type Icon

Building Human-in-the-Loop Machine Learning Systems – Fashion 2.0

Steven Bourke, Outfittery
Session Type Icon

Next generation AI: Emotional Artificial Intelligence based on audio

Dagmar Schuller, audEERING GmbH
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
Session Type Icon

Honey Bee Conservation using Deep Learning

Thiago da Silva Alves, JArchitects