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.
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.
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.
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.
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.
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.
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.
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.
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.
Giving up ownership over their data even for collaboration for building machine learning models is what organisations fret about. Daniel Zakrisson talked about federated machine learning as a solution for this predicament at Data Innovation Summit 2019.
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