Pipelines vs scikit-learn: What are the differences?
Pipelines: 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; scikit-learn: Easy-to-use and general-purpose machine learning in Python. scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
Pipelines and scikit-learn can be categorized as "Machine Learning" tools.
Pipelines and scikit-learn are both open source tools. scikit-learn with 36K GitHub stars and 17.6K forks on GitHub appears to be more popular than Pipelines with 944 GitHub stars and 247 GitHub forks.
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What is Pipelines?
What is scikit-learn?
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