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ProductizeML: Assisting Your Team to Better Build ML Products

Posted Jan 13, 2021 | Views 269
# Interview
# docs.productizeml.com
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Adria Romero
Founder and Instructor @ ProductizeML

Adria is an AI, ML, and product enthusiast with more than 4 years of professional experience on his mission to empower society with data and AI-driven solutions.

Born and raised in the beautiful and sunny Barcelona, he began his journey in the AI field as an applied researcher at the Florida Atlantic University, where he published some of the first deep learning works in the healthcare sector. Attracted by the idea of deploying these ideas to the real world, he then joined Triage, a healthcare startup building healthcare solutions powered by AI, such a smartphone app able to detect over 500 skin diseases from a picture. During this time, he has given multiple talks at conferences, hospitals, and institutions such as Novartis and Google. Previously, he interned at Huawei, Schneider Electric, and Insight Center for Data Analytics.

Early this year, he started crafting ProductizeML, an instruction and interactive guide for teams building Machine Learning products where he and a team of AI & product specialists carefully prepare content to assist on the end-to-end ML lifecycle.

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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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SUMMARY

In this talk we will talk about: - Motivations and mission behind ProductizeML. - Common friction points and miscommunication between technical and management/product teams, and how to bridge these gaps. - How to define ML product roadmaps, (and more importantly, how to get it signed off by all your team). - Best practices when managing the end-to-end ML lifecycle.

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