A case for declarative configurations for ML training
Using config files to specify infrastructure for training isn't widely practiced in the machine learning community, but it helps a lot with reproducibility.
May 17, 20205 Mins Read
Using config files to specify infrastructure for training isn't widely practiced in the machine learning community, but it helps a lot with reproducibility.
ZenML makes it easy to setup training pipelines that give you all the benefits of cached steps.
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.
