Author: Sascha Grunert, SUSE Software Solutions
Editor's note: Sascha is part of SIG Release and is working on many other different container runtime related topics. Feel free to reach him out on Twitter @saschagrunert.
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.
Introduction Getting the Data Exploring the Data Labels, Labels, Labels Building the Machine Learning Model Doing some first Natural Language Processing (NLP) Creating the Multi-Layer Perceptron (MLP) Model Training the Model A first Prediction Automate Everything Automatic Labeling of new PRs Summary Introduction Choosing the right steps when working in the field of data science is truly no silver bullet.
Dr. Roy Schestowitz (罗伊)
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