Do You Dare Run Your ML Experiments in Production? with Ville Tuulos - #523
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Navigating Metaflow and MLOps Challenges
This chapter examines the development and utility of Metaflow as a vital tool for enhancing productivity in data science and machine learning. It addresses common challenges faced by data scientists, such as data access and dependency management, and illustrates Metaflow's role in facilitating seamless integrations with existing infrastructures. The conversation also explores technical aspects including model monitoring, versioning, and the evolving relationship between tools like Kubernetes and AWS in the context of machine learning operations.
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