The following Q&A occurred during the Everyday AI Conference Paris, during which Michelin hosted two sessions.
The use of AI within an industrial group has several advantages: faster product design, better quality results, and improved industrial performance. What is essential to us is our speed in scaling the gains obtained through AI within our 85 industrial sites around the world.
Dataiku is used daily in our industrial sites as well as in our central teams on broad themes impacting quality, maintenance, machine availability, supply chain, and energy consumption.
A good example of the use of Dataiku is predictive maintenance: By collecting and analyzing the data provided by the machines, we are able to predict breakdowns before they occur and alert the maintenance technician so that he or she repairs them preventively.
For me, Everyday AI is about empowering extraordinary people to change the way they do things based on data.
Novartis moved from manual spreadsheet calculations to informed decision-making with Dataiku and harnessed the Dataiku LLM Mesh to revolutionize healthcare market research.
Read moreLearn how JK Lakshmi's data team uses Dataiku to improve and save time on reports, and make operational tasks more efficient.
Learn MoreFives Group boosts efficiency and drives innovation by integrating AI throughout their internal processes as well as within externally facing client-focused processes.
Learn MoreLearn about the culture of data science at scale that Oshkosh Corporation has developed using modern software tools like Dataiku — a culture that has enabled significant cost savings and performance improvements over a variety of solutions for the business and their customers.
Learn MoreTechnical Safety BC leverages Dataiku to deliver quality safety oversight with a small data science team, ultimately improving predictive performance for risk factors by 85%.
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