Interview
summary
Building MLflow as an open source machine learning pipeline tool that companies actually use, with real bug reports coming in that validate production use and surface pain points. It enforces best practices for productionizing ML pipelines, documents what components it tracks, and shows how teams at Databricks use Scala to integrate with it. Expect concrete examples of deployment workflows, versioning, and the specific gaps users report.
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