Shingo Omura
Software Architect at LY Corporation
Tokyo, Japan
Summary
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RockstarShingo Omura is a Software Architect based in Tokyo with 15 years of experience designing and implementing distributed, cloud-native systems. He blends deep back-end engineering—contributions to containerd, Marathon, and Datadog integrations—with MLOps and Kubernetes expertise demonstrated in Kubeflow and kubeadm-related projects. Comfortable across the stack, he has shipped CRI/user-management fixes in container runtimes, built event subscription services for orchestration platforms, and integrated JMX-based metrics for production monitoring. His work on ML-class exercises and Kubeflow openmpi shows a practical grasp of ML algorithms and production job orchestration, not just infrastructure. A habitual open-source contributor, he favors pragmatic, test-driven improvements that make complex distributed systems more reliable and operable. Colleagues can expect an engineer who pairs systems-level thinking with hands-on coding and configuration hygiene.
15 years of coding experience
Github Skills (51)
monitoring10
hbase10
kubernetes10
api-rest10
container10
docker10
data-collection10
python10
scripting10
api-design10
restful-api10
machine-learning10
bash10
matlab10
openmpi10
Programming languages (20)
Github contributions (5)
everpeace/ml-class-assignments
Oct 2011 - Aug 2017
Programming Exercises on http://ml-class.org
Role in this project:
ML Engineer
ML EngineerContributions:37 commits, 2 PRs, 2 pushes in 5 years 11 months
Contributions summary:Shingo's primary contribution involves implementing and refining machine-learning-related exercises for an online machine learning class. This is evidenced by the creation and modification of various `.m` files focused on topics such as linear regression, logistic regression, neural networks, and anomaly detection. The code changes encompass cost function calculations, gradient descent implementations, model prediction, and the application of regularization techniques, all within the context of the ml-class.org platform.
pythonmachine-learning
kubeflow/kubeflow
Apr 2018 - Jan 2019
Machine Learning Toolkit for Kubernetes
Role in this project:MLOps Engineer
MLOps EngineerContributions:20 commits, 26 PRs, 89 comments in 9 months
Contributions summary:Shingo primarily contributed to the `openmpi` component, focusing on improvements related to Kubernetes integration and job execution. They addressed issues related to cluster DNS, scheduler configuration, and resource management. Furthermore, they implemented features like customizable image pull secrets and support for non-root users, enhancing the flexibility and usability of the openmpi jobs within the Kubeflow environment.
pythondata-sciencenotebookmachine-learningmlops
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