Episode Details

MLOps Coffee Sessions #123 with Gleb Abroskin, Machine Learning Engineer at Funcorp, RECOMMENDER SYSTEM: Why They Update Models 100 Times a Day, co-hosted by Jake Noble.

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// Abstract
FunCorp was a top 10 app store. It was a very popular app that had a ton of downloads and just memes. They need a recommendation system on top of that. Memes are super tricky because they're user-generated and they evolve very quickly. They're going to live and die by the Recommender System in that product.
It's incredible to see FunCorp's maturity. Gleb breaks down the feature store they created and the velocity they have to be able to create a whole new pipeline in a new model and put it into production after only a month!

// Bio
Gleb makes models go brrrrr. He doesn't know what is expected in this field, to be honest, but Gleb has experience in deploying a lot of different ML models for CV, speech recognition, and RecSys in a variety of languages (C++, Python, Kotlin), serving millions of users worldwide.

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// Related Links
Putting a two-layered recommendation system into production -
https://medium.com/@FunCorp/putting-a-two-layered-recommendation-system-into-production-b8caaf61393d
Practical Guide to Create a Two-Layered Recommendation System -
https://medium.com/@FunCorp/practical-guide-to-create-a-two-layered-recommendation-system-5486b42f9f63
Ten Mistakes to Avoid When Creating a Recommendation System -
https://medium.com/@FunCorp/ten-mistakes-to-avoid-when-creating-a-recommendation-system-8268ed60aeba
Applying Domain-Driven Design and Patterns: With Examples in C# and .NET, 1st Edition by Jimmy Nilsson:
https://www.amazon.com/Applying-Domain-Driven-Design-Patterns-Examples/dp/0321268202

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