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Argo Workflows

Posted Jul 28
# Argo Workflows
# MLOps Stack
# Artifact Storage
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SPEAKER
Kemal Tugrul Yesilbek
Kemal Tugrul Yesilbek
Kemal Tugrul Yesilbek
Senior Machine Learning Engineer @ Beat

Kemal is a Senior Machine Learning Engineer at Beat, one of the fastest-growing ride-hailing apps in Latin America. He studied software engineering and machine learning. During his time in academia, he published machine learning solutions approaching human-level performance.

Kemal started his career as a data scientist. He founded Elify.io, a skill assessment tool for data-driven roles, which resulted in an exit. He is working as a machine learning engineer for the past years, delivering end-to-end machine learning backed solutions.

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Kemal is a Senior Machine Learning Engineer at Beat, one of the fastest-growing ride-hailing apps in Latin America. He studied software engineering and machine learning. During his time in academia, he published machine learning solutions approaching human-level performance.

Kemal started his career as a data scientist. He founded Elify.io, a skill assessment tool for data-driven roles, which resulted in an exit. He is working as a machine learning engineer for the past years, delivering end-to-end machine learning backed solutions.

+ Read More
SUMMARY

One of the most popular, and useful, ways to productionize a machine learning solution is scheduled batch workflows. In this approach, we deliver predictions in regular intervals. There are many tools available allowing you to construct and schedule your workflows. When there are many options, it can be difficult to choose.

In this session, we talk about Argo Workflows for batch workflows; how to build them; and why you may want to adopt them in your MLOps stack.

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