ML in Production: A DS from Ubisoft Perspective // Jean-Michel Daignan // MLOps Podcast #151

MLOps Coffee Sessions #151 with Jean-Michel Daignan, ML in Production: A DS from Ubisoft Perspective, co-hosted by Abi Aryan.

// Abstract
As a data scientist himself, Jean-Michel has a unique perspective on the needs of data scientists when it comes to platform development. They're also developing tools (like SDK) that streamline the model development process. The team is focused on tying machine learning products back to business use cases and the ROI they provide. Jean-Michel also discusses the use of generative AI and the importance of balancing delivering value and building things quickly. Jean-Michel's blog posts on the topic are recommended for further reading.

// Bio
The author of the blog "the-odd-dataguy.com" has been a data scientist for over 4.5 years at Ubisoft. Prior to joining the video game industry, Jean-Michel had a background in engineering from France and had previously worked in the energy sector. The blog focuses on topics related to data and machine learning, showcasing the author's expertise in the field.

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// Related Links
Blog page: https://www.the-odd-dataguy.com/
Bringing Machine Learning to Production at Ubisoft (PydataMTL June22): https://www.the-odd-dataguy.com/2022/12/29/recap_pydata_mtl_june22/

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Abi on LinkedIn: https://www.linkedin.com/in/goabiaryan/
Connect with Jean-Michel on LinkedIn: https://www.linkedin.com/in/jeanmicheldaignan/

Timestamps:
[00:00] Jean-Michel's preferred beverage
[00:19] Jean-Michel Daignan's background
[00:28] Takeaways
[04:30] Rate us and share the podcasts with your friends!
[05:37] Jean-Michel's projects at Ubisoft
[07:48] Jean-Michel's success as a Data Scientist
[09:45] Ubisoft basics
[10:40] Jean-Michel's success from the downfalls of being a data scientist
[12:18] Building for data scientists' considerations
[13:57] Differences in designing for data scientists in general
[16:35] End twin pipelines and their functions
[19:35] Major problems doing maintenance
[20:53] Data quality ownership
[22:33] Monitoring levels
[24:25] Locomotive systems
[26:14] Merlin
[29:12] DS storage systems
[31:09] Feature stores batch or streaming?
[32:19] Bringing Machine Learning to Production at Ubisoft blog post
[35:10] Features and recommendation systems
[37:03] Playing games
[38:21] Play data = play personalities
[39:42] Deep learning in all the diffusion models or the foundation models
[43:06] Servicing data scientists' needs
[45:28] Ubisoft's data volume
[48:00] Wrap up

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