LVMH: Centralization & Personalization — A Hybrid Approach to AI

Discover how LVMH centralized and customized deployment of AI algorithms for its luxury goods houses.

The following Q&A occurred during Everyday AI Conference Paris, during which LVMH Moët Hennessy Louis Vuitton hosted a session on centralization and personalization of AI. During this talk, Aurélien Gascon, Group Head of Enterprise Architecture & Data Technology, and Axel de Goursac, Group Head of Data Science,  presented their strategy of using pre-packaged algorithms to ensure data autonomy, while respecting the singularity of each brand and the different levels of data maturity within the group.

Interview of Aurélien Gascon, Group Head of Enterprise Architecture & Data Technology at LVMH

Listen to the LVMH Everyday AI Paris Session


How Do You Use Dataiku to Accelerate AI?

Aurélien Gascon: 

We use AI to improve customer experience for our customers. That’s why identifying our best future customers is critical. Dataiku helps develop and scale these kinds of algorithms across our different brands. 

Axel de Goursac:

I have a project on fostering product discovery and recommendations to our clients in a personalized way. We personalize these based on the history of interaction of the client with the brand via algorithms of a recommender system. 


What Challenges Does Dataiku Help You Overcome?

Aurélien Gascon: 

Scaling across different brands can be quite difficult due to differences in contexts and products. In the end, having a core development of those algorithms and being able to adapt to different contexts is very important. Dataiku helps us a lot in achieving this.

Axel de Goursac: 

We use Dataiku in our global strategy to overcome the challenges of deploying AI at scale in our houses. 

  1. The first challenge is reusability. We put high energy and resources to build efficient algorithms for one project and then we reuse and leverage this kind of performance. This is required to scale. 
  2. The second one is about industrialization. To ensure a safe run, we invest a lot of effort into industrialization and adopting MLOps best practices. This is necessary to have things that will work for months and years.
  3. The third challenge is about adoption. Having Dataiku, a collaborative platform that gathers different profiles and allows adoption and appropriation of the models is key because, without adoption, you don’t generate value.


What Main Benefit Does Dataiku Bring You? 

Axel de Goursac: 

Suppose you are a confirmed data scientist and you like to code with an advanced AI library in your PyCharm or VS Code environment. In Dataiku, you can integrate all the code you have already done via plugins. This allows you to expose your code and the different parameters. You don’t need to hardcode them. So this is a very important benefit and it also fosters reusability for different use cases. 


What Does Everyday AI Mean to You? 

Aurélien Gascon: 

Everyday AI is a perfect opportunity for all AI practitioners to meet together to understand what’s next, what has been done well, and what could be difficult to achieve. 

Axel de Goursac: 

Everyday AI means that our employees at LVMH are using AI every day, in their daily professional lives. Thanks to our algorithms, sometimes they don’t even notice it, but they are actually using AI to augment their capabilities!

Interview of Axel de Goursac, Group Head of Data Science at LVMH

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