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. He talks about the non-invasive approach his team is taking to bring people onto the platform and their SDK, Merlin. The team is focused on tying machine learning products back to business use cases and the ROI they provide. Abby and Jean-Michel also discuss 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.

// MLOps Jobs board

jobs.mlops.community

// MLOps Swag/Merch

https://mlops-community.myshopify.com/

// Related Links

Blog page: https://www.the-odd-dataguy.com/

Bringing Machine Learning to Production at Ubisoft (PydataMTL June 22): https://www.the-odd-dataguy.com/2022/12/29/recap_pydata_mtl_june22/

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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