ZenML's Pipeline Deployments transform pipelines into persistent HTTP services with warm state, instant rollbacks, and full observability—unifying real-time AI agents and classical ML models under one production-ready abstraction.
Using config files to specify infrastructure for training isn't widely practiced in the machine learning community, but it helps a lot with reproducibility.
Software engineering best practices have not been brought into the machine learning space, with the side-effect that there is a great deal of technical debt in these code bases.
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