The data science process is not free of friction, especially when acquiring, preparing and cleaning data. But even when data is readily available, one might want to use it to train a number of ML models to perform model selection.
And what happens, if an update is available and the whole process needs to be run again? Optimally with a tested model that is put into production in a canary deployment workflow?
The Kubeflow project is a cloud native machine learning toolkit supporting experimentation, pipeline building and deployment. As a cloud native tool it is composed of a set of deployments and services, from managing Jupyter notebooks, pipelines, dashboard and serving tools.
Sascha and Markus took us on an amazing tour using a real world use case, showcasing the various parts of Kubeflow and how they fit together.
Rather than repeating the details here, check out the blog post My exciting journey into Kubernetes’ history —
A story of data science-ing 90,000 GitHub issues and pull requests by using Kubeflow, TensorFlow, Prow and a fully automated CI/CD pipeline.
The accompanying repository can be found at: https://github.com/kubernetes-analysis/kubernetes-analysis.