DMTK vs Pipelines: What are the differences?
Developers describe DMTK as "Microsoft Distributed Machine Learning Tookit". DMTK provides a parameter server based framework for training machine learning models on big data with numbers of machines. It is currently a standard C++ library and provides a series of friendly programming interfaces. On the other hand, Pipelines is detailed as "Machine Learning Pipelines for Kubeflow". Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK.
DMTK and Pipelines can be primarily classified as "Machine Learning" tools.
DMTK and Pipelines are both open source tools. It seems that DMTK with 2.69K GitHub stars and 595 forks on GitHub has more adoption than Pipelines with 944 GitHub stars and 247 GitHub forks.