MLOps Coffee Sessions #114 with Marc Lindner, Co-Founder, COO, and Amr Mashlah, Head of Data Science of eezylife Inc., Product Enrichment and Recommender Systems, co-hosted by Skylar Payne.

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// Abstract
The difficulties of making multi-modal recommender systems. How can it be easy to know something about a user but very hard to know the same thing about a product and vice versa? For example, you can clearly know that a user wants an intellectual movie, but it is hard to accurately classify a movie as intellectual and fully automated.

// Bio
Marc Lindner has a background in Knowledge Engineering. He's always extremely product-focused with anything to do with Machine Learning.   
Marc built several products working together with companies such as Lithium Technologies, etc., and then co-founded eezy.

Amr Mashlah
Amr is the head of data science at eezy, where he leads the development of their recommender engine. Amr has a master's degree in AI and has been working with startups for 6 years now.

// MLOps Jobs board  

jobs.mlops.community

MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
Children of Time book by Adrian Tchaikovsky:  
https://www.amazon.com/Children-Time-Adrian-Tchaikovsky/dp/0316452505

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Skylar on LinkedIn: https://www.linkedin.com/in/skylar-payne-766a1988/
Connect with Marc on LinkedIn: https://www.linkedin.com/in/marc-lindner-883a0883/
Connect with Amr on LinkedIn: https://www.linkedin.com/in/mashlah/

Timestamps:

[00:00] Takeaways

[04:20] Introduction to Marc Lindner and Amr Mashlah

[06:08] eezylife Inc.

[08:43] Integration with different tools

[10:13] Richer data for eezy

[12:58] Challenges with different providers

[15:25] Labeling solutions

[16:00] Maodal.com

[18:18] Handling multi-modal recommendation

[20:11] Figuring out the process of ingesting the data

[23:16] Ontology of sorts

[27:21] Talking in-depth about technical pieces

[28:40] Handling cold start

[31:42] Bad recommendations

[37:24] Curation vs hard-coded rules

[39:53] Social features

[41:57] eezy's vision

[45:12] Suggested recommendations

[46:48] Preferences from inferred interactions

[51:45] Ethical considerations

[54:19] Wrap up