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MLOps at the Crossroads

Posted Jan 16, 2024 | Views 411
# MLOps
# Kentauros AI
# LLMLOps
# AIMedic
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SPEAKERS
Patrick Barker
Patrick Barker
Patrick Barker
CTO @ Kentauros AI

When Patrick is not occupied with building his AI company, he enjoys spending time with his wonderful kids or exploring the hills of Boulder.

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When Patrick is not occupied with building his AI company, he enjoys spending time with his wonderful kids or exploring the hills of Boulder.

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Farhood Etaati
Farhood Etaati
Farhood Etaati
MLOps/Platform Team Lead @ AIMedic

MLOps engineer at AIMedic. Studied EE at Uni of Tehran, started as a data scientist, and pivoted to software engineering. Currently working on on-premise MLOps platform development suitable for Iran's infrastructure.

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MLOps engineer at AIMedic. Studied EE at Uni of Tehran, started as a data scientist, and pivoted to software engineering. Currently working on on-premise MLOps platform development suitable for Iran's infrastructure.

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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

MLOps is at a crossroads. The ever-increasing excitement for LLMs' ability to solve some interesting real-world problems has made many people interested in applying these models in new applications which comes with its own challenges, that have upstarted the term "LLMLOps". But how much of those challenges are not a newer representation of what older-gen ML models had to deal with in the production, and the question arises whether developing "new" specialized tools to address these applications actually provides any substantial value for the sustainability of the field in general. Tools are coming and going at a rate that makes many technical people skeptical of adopting newer tools. What can we do as a community to alleviate these issues? Why OSS MLOps is lacking behind and how VC money is contributing to that?

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