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RECOMMENDER SYSTEM: Why They Update Models 100 Times a Day

Posted Sep 15, 2022 | Views 967
# FunCorp
# Recommender Systems
# A/B Testing
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SPEAKERS
Gleb Abroskin
Gleb Abroskin
Gleb Abroskin
ML Engineer @ FunCorp

Gleb make 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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Gleb make 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.

+ Read More
Demetrios Brinkmann
Demetrios Brinkmann
Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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SUMMARY

FunCorp’s iFunny app was one of the top 10 most popular entertainment apps in the US. It is still a very popular app that has 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!

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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