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Why and When to Use Kubeflow for MLOps

Posted Jul 07, 2022 | Views 813
# KubeFlow
# ML Engineering
# Kubernetes
# mavenwave.com
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Ryan Russon
Manager, MLOps and Data Science @ Maven Wave Partners

From serving as an officer in the US Navy to Consulting for some of America's largest corporations, Ryan has found his passion in the enablement of Data Science workloads for companies and teams.

Having spent years as a data scientist, Ryan understands the types of challenges that DS teams face in scaling, tracking, and efficiently running their workloads.

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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

Kubeflow is an excellent platform if your team is already leveraging Kubernetes and allows for a truly collaborative experience.

Let’s take a deep dive into the pros and cons of using Kubeflow in your MLOps.

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