When your business is serving customers in 6000 cities, in 64 countries and millions of riders, you have to make sure you live and breath customer satisfaction. Uber, the global tech leader in ride-sharing, can teach us a thing or two with how they manage to keep all their riders and drivers happy.
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.
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.
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.
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