Democratising model building at ING Bank
Data science in banking may sound a bit boring. However, data scientists have had their hands full with working on some really interesting challenges that banks have been experiencing these past several years.
How Equinor deploys machine learning at scale
The usual way of implementing machine learning and AI in an organisation starts with doing proofs of concept and pilot projects for specific use cases. But this approach falls short of delivering any ROI for the business to be able to justify the cost.
How to build interpretable machine learning models
As AI and machine learning are becoming an increasing part of our lives and businesses, our need to interpret machine learning models so we can trust them and deploy them with confidence is also growing.
How VR Group uses machine learning to predict passenger number
The passenger number is a crucial figure in railway operations that impact the overall functioning of the commuter railway traffic. But in the case of VR Group, Finland’s government-owned railway company, with over 14 million passengers carried on long-distance rail services only in 2019, predicting passenger number requires additional helping hand by machine learning.
Data and AI Innovators – Mats Stellwall
Mats Stellwall, Data Scientist at Oracle and a Data Innovation Summit 2019 speaker, shared his observations on the developments with AI, machine learning and deep learning.
Why and where The LEGO Group deploys machine learning models
Francesc Joan Riera, Applied Machine Learning Engineer, Data Engineering at The LEGO Group, is coming to the Data Innovation Summit 2020 to demonstrate how they train, maintain and update deployed machine learning models for LEGO’s Moderation Service.
Data and AI Innovators – Clair Sullivan
Clair demonstrated a deep learning method that can be used to detect duplicate code at GitHub, which is more than half of all the code on the platform.
Data and AI Innovators – Gábor Stikkel
The prolific career of a data scientist can have its roots in an innocent child’s dream to predict soccer results. Or at least that was the case for Gábor Stikkel, an accomplished Senior Data Scientist at HID Global and speaker at the Data Innovation Summit 2019.
How Klarna personalises the checkout experience with data and ML
We are going to see Klarna’s story from an engineer’s and data scientist’s point of view, where first we look into their data organisation and structure, and later we’ll devote the white space to their machine learning models for superb purchase experience.
Where are we with AI and ML so far: A Silicon Valley perspective
As Silicon Valley is the main cradle of the largest high-tech corporation, including Apple, it’s only fitting to have a Silicon Valley perspective on where we stand with digitalisation and emerging technologies like AI, machine learning and deep learning. Bhairav Mehta, Data Science Manager Apple gave a comprehensive overview of the latest developments and trends, but also covered important historical breakthroughs that lead to the current state of AI and ML.
Data and AI Innovators – Jorge Davila Chacon
Jorge Davila Chacon discussed the RL application for debt collection at Data Innovation Summit 2017. But a time-lapse of almost 3 years is a lifetime in the machine learning world. This was the reason we invited Jorge to talk about recent developments in AI and ML, and particularly in reinforcement learning, not only from a technical side, but also from a social aspect of the adoption of these technologies.
Business
Democratising model building at ING Bank
Data science in banking may sound a bit boring. However, data scientists have had their hands full with working on some really interesting challenges that banks have been experiencing these past several years.
How Equinor deploys machine learning at scale
The usual way of implementing machine learning and AI in an organisation starts with doing proofs of concept and pilot projects for specific use cases. But this approach falls short of delivering any ROI for the business to be able to justify the cost.
How to build interpretable machine learning models
As AI and machine learning are becoming an increasing part of our lives and businesses, our need to interpret machine learning models so we can trust them and deploy them with confidence is also growing.
How VR Group uses machine learning to predict passenger number
The passenger number is a crucial figure in railway operations that impact the overall functioning of the commuter railway traffic. But in the case of VR Group, Finland’s government-owned railway company, with over 14 million passengers carried on long-distance rail services only in 2019, predicting passenger number requires additional helping hand by machine learning.
Data and AI Innovators – Mats Stellwall
Mats Stellwall, Data Scientist at Oracle and a Data Innovation Summit 2019 speaker, shared his observations on the developments with AI, machine learning and deep learning.
Why and where The LEGO Group deploys machine learning models
Francesc Joan Riera, Applied Machine Learning Engineer, Data Engineering at The LEGO Group, is coming to the Data Innovation Summit 2020 to demonstrate how they train, maintain and update deployed machine learning models for LEGO’s Moderation Service.
Data and AI Innovators – Clair Sullivan
Clair demonstrated a deep learning method that can be used to detect duplicate code at GitHub, which is more than half of all the code on the platform.
Data and AI Innovators – Gábor Stikkel
The prolific career of a data scientist can have its roots in an innocent child’s dream to predict soccer results. Or at least that was the case for Gábor Stikkel, an accomplished Senior Data Scientist at HID Global and speaker at the Data Innovation Summit 2019.
How Klarna personalises the checkout experience with data and ML
We are going to see Klarna’s story from an engineer’s and data scientist’s point of view, where first we look into their data organisation and structure, and later we’ll devote the white space to their machine learning models for superb purchase experience.
Where are we with AI and ML so far: A Silicon Valley perspective
As Silicon Valley is the main cradle of the largest high-tech corporation, including Apple, it’s only fitting to have a Silicon Valley perspective on where we stand with digitalisation and emerging technologies like AI, machine learning and deep learning. Bhairav Mehta, Data Science Manager Apple gave a comprehensive overview of the latest developments and trends, but also covered important historical breakthroughs that lead to the current state of AI and ML.
Tech
Democratising model building at ING Bank
Data science in banking may sound a bit boring. However, data scientists have had their hands full with working on some really interesting challenges that banks have been experiencing these past several years.
How Equinor deploys machine learning at scale
The usual way of implementing machine learning and AI in an organisation starts with doing proofs of concept and pilot projects for specific use cases. But this approach falls short of delivering any ROI for the business to be able to justify the cost.
How to build interpretable machine learning models
As AI and machine learning are becoming an increasing part of our lives and businesses, our need to interpret machine learning models so we can trust them and deploy them with confidence is also growing.
How VR Group uses machine learning to predict passenger number
The passenger number is a crucial figure in railway operations that impact the overall functioning of the commuter railway traffic. But in the case of VR Group, Finland’s government-owned railway company, with over 14 million passengers carried on long-distance rail services only in 2019, predicting passenger number requires additional helping hand by machine learning.
Data and AI Innovators – Mats Stellwall
Mats Stellwall, Data Scientist at Oracle and a Data Innovation Summit 2019 speaker, shared his observations on the developments with AI, machine learning and deep learning.
Why and where The LEGO Group deploys machine learning models
Francesc Joan Riera, Applied Machine Learning Engineer, Data Engineering at The LEGO Group, is coming to the Data Innovation Summit 2020 to demonstrate how they train, maintain and update deployed machine learning models for LEGO’s Moderation Service.
Data and AI Innovators – Clair Sullivan
Clair demonstrated a deep learning method that can be used to detect duplicate code at GitHub, which is more than half of all the code on the platform.
Data and AI Innovators – Gábor Stikkel
The prolific career of a data scientist can have its roots in an innocent child’s dream to predict soccer results. Or at least that was the case for Gábor Stikkel, an accomplished Senior Data Scientist at HID Global and speaker at the Data Innovation Summit 2019.
How Klarna personalises the checkout experience with data and ML
We are going to see Klarna’s story from an engineer’s and data scientist’s point of view, where first we look into their data organisation and structure, and later we’ll devote the white space to their machine learning models for superb purchase experience.
Where are we with AI and ML so far: A Silicon Valley perspective
As Silicon Valley is the main cradle of the largest high-tech corporation, including Apple, it’s only fitting to have a Silicon Valley perspective on where we stand with digitalisation and emerging technologies like AI, machine learning and deep learning. Bhairav Mehta, Data Science Manager Apple gave a comprehensive overview of the latest developments and trends, but also covered important historical breakthroughs that lead to the current state of AI and ML.