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Non-traditional Career Paths in MLOps

Posted Feb 07, 2023 | Views 491
# Career paths
# Stakeholder alignment
# Ambiguity in ML
# amazon.com
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Matthew Dombrowski
Sr Product Manager - Technical @ Amazon

Matt has performed a number of MLOps positions including Solutions Consultant, Solutions Architect, and Product Manager from startups to large organizations. In his current role, Matt builds tools to help social media influencers discover unique and exciting Amazon products to recommend to their audiences.

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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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Mihail Eric
Co-founder @ Storia AI

Mihail is a co-CEO of Storia AI, an early-stage startup building an AI-powered creative assistant for video production. He has over a decade of experience researching and engineering AI systems at scale. Previously he built the first deep-learning dialogue systems at the Stanford NLP group. He was also a founding member of Amazon Alexa’s first special projects team where he built the organization’s earliest large language models. Mihail is a serial entrepreneur who previously founded Confetti AI, a machine-learning education company that he led until its acquisition in 2022.

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SUMMARY

Let's explore the different aspects of ML and data roles and the variety of responsibilities each role entails! This conversation emphasizes the need for understanding the unique insights each role provides and the similarities in responsibilities and soft skills that are required across different roles.

This episode also highlights the significance of stakeholder alignment in the context of working in big companies and the importance of navigating these complexities for a successful career in ML.

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