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ML Platform Tradeoffs and Wondering Why to Use Them // Javier Mansilla // MLOps Coffee Sessions #88

54 min • 28 mars 2022

MLOps Coffee Sessions #88 with Javier Andres Mansilla, ML Platform Tradeoffs and Wondering Why to Use Them.

// Abstract
Javier runs ML Platform at Mercado Libre. We’re here with Javier because he’s going to tell us about what the ML platform at Mercado Libre looks like granularly, talk about its purpose, lessons, wins, and future improvements, and share with us some of the most challenging use cases they’ve had to engineer around.

// Bio
During the last 3 years building the internal ML platform for Mercado Libre (NASDAQ MELI), the biggest company in Latam, and the eCommerce & fintech omnipresent solution for the continent.

Seasoned entrepreneur and leader, Javier was co-founder and CTO of Machinalis, a hi-end company building Machine Learning since 2010 (yes, before the breakthrough of neuralnets). When Machinalis got acquired by Mercado Libre, that small team evolved to enable Machine Learning as a capability for a tech giant with more 10k devs, impacting the lives of almost 100 million direct users.

On a daily basis, Javier leads not only the tech and product roadmap of their Machine Learning Platform, but also their users' tracking system, the AB Testing framework, and the open-source office.

Javier loves hanging out with family and friends, python, biking,  football, carpentry, and slow-paced holidays in nature!

// MLOps Jobs board  
https://mlops.pallet.xyz/jobs 

// Related Links

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Javier on LinkedIn: https://www.linkedin.com/in/javimansilla/

Timestamps:
[00:00] Introduction to Javier Andres Mansilla
[02:18] Refresher to what Mercado Libre is
[06:16] Centralization of Machine Learning platform at Mercado Libre
[11:58] Mercado Libre's working size
[16:15] Hitting the scale
[21:07] Driving ML platform vision and team's business metrics  
[28:23] Education process of how to use machine learning on the platform
[36:49] Composition of the team members and finding the right people
[43:05] Stakeholders
[45:32] Decision making
[48:51] Wrap up
[49:52] Bonus from Javier

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