Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK
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Improving infrastructure efficiency with unsupervised algorithms

Alexandre Hubert (Dataiku)
16:3517:15 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Average rating: *****
(5.00, 1 rating)

Who is this presentation for?

  • Data scientists and executives



Prerequisite knowledge

  • Familiarity with data science (useful but not required)

What you'll learn

  • Understand the key technical steps to ensure the success of industrial projects and how to deploy these systems into existing business processes


Water, sewage, energy, and public transportation systems in major cities are aging fast and are exceedingly difficult to update. Ceasing service to these critical utilities isn’t an option, as a mere hour’s delay can cause serious issues to residents and businesses in the area. The best way to improve these systems is to optimize their efficiency. However, the hardest part of optimizing infrastructure isn’t the algorithms or model creation but incorporating the necessary sensors and workflow adjustment into associated business processes.

Alexandre Hubert outlines a real-world example of energy sector optimization through automatic flagging of unnecessary field visits. in partnership with natural gas provider GRDF, Dataiku worked to optimize the manual process of qualifying addresses to visit and ultimately save GRDF time and money. Dataiku brought the system from an unsupervised problem with messy data into a successful automated process.

Alexandre discusses the challenges encountered along the way, including working with business experts to create a training set from disparate structured and unstructured data sources, and shares a model for how to implement unsupervised algorithms in a scalable way. This is the best first step in long-term infrastructure improvement—and a critical one as cities become more densely populated.

Photo of Alexandre Hubert

Alexandre Hubert


Alexandre Hubert is one of Dataiku’s top data scientists, but he began his career in a very different domain: working as a trader. He soon realized that with the huge amount of data out there, it was possible (and fun) to resolve problems using real-life data. Since becoming a data scientist, Alexandre has worked on a range of use cases, from creating models that predict fraud to building specific recommendation systems. He especially loves using deep learning with text or sports data. Even when he’s having fun with friends, Alexandre sees numbers and patterns everywhere, bringing him quickly back to his laptop to try out new ideas.