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We are discussing the results from a semi-structured interview study of ML engineers spanning different organizations and applications to understand their workflow, best practices, and challenges. The authors found that "successful MLOps practices center around having high velocity, validating as early as possible, and maintaining multiple versions of models for minimal production downtime."

In addition, we invited a panel of industry experts to discuss MLOps pain points and check in on their MLOps tooling advice.

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AI/ML
Machine Learning
Data Engineering
Intellectual Discussions
Open Source

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