Pipelines vs Yellowbrick: 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; Yellowbrick: Visual analysis and diagnostic tools to facilitate machine learning model selection. It is a suite of visual diagnostic tools called "Visualizers" that extend the scikit-learn API to allow human steering of the model selection process. In a nutshell, it combines scikit-learn with matplotlib in the best tradition of the scikit-learn documentation, but to produce visualizations for your machine learning workflow.
Pipelines and Yellowbrick can be categorized as "Machine Learning" tools.
Pipelines is an open source tool with 1.51K GitHub stars and 534 GitHub forks. Here's a link to Pipelines's open source repository on GitHub.