kakaocloud-edu/tutorial
Mar 2026 – Mar 2026
1 Month active
Languages Used
BashMarkdownPythonSQLYAML
Technical Skills
Database ManagementDevOpsDockerJupyterJupyter NotebookKubeflow
llokr1
PROFILE
Llokr1
Contributed to the kakaocloud-edu/tutorial repository by delivering six new features focused on enhancing lab reliability, security, and machine learning workflows within a Kubernetes-based environment. Work included refining Kubernetes lab setup scripts to require sudo for key operations, updating Docker image paths for compatibility, and strengthening user privilege policies across manifests. Developed a Jupyter notebook for end-to-end Fashion MNIST model training and serving with Kubeflow, incorporating data preprocessing and secure environment variable handling. Updated MySQL version references throughout documentation to ensure consistency. Leveraged Python, SQL, and YAML to improve onboarding speed, reproducibility, and overall usability of the tutorial platform.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
6
Lines of code
1,489
Activity Months1
Your Network
5 peopleSame Organization
@gachon.ac.kr2
Shared Repositories
3
Work History
March 2026
11 Commits • 6 Features
Mar 1, 2026March 2026 monthly summary for kakaocloud-edu/tutorial. Deliveries focused on improving lab reliability, security, and end-to-end ML workflows in the Kubernetes-based tutorial environment. Key features include updates to Lab11 documentation and image references, Kubernetes lab environment refinements, Docker image path updates, security hardening of user privilege policies, and the addition of a Fashion MNIST Kubeflow notebook, along with MySQL version references updated across documentation. These changes enhance onboarding speed, reproducibility of lab environments, and overall platform security and usability.
11 Commits • 6 Features
Mar 1, 2026March 2026 monthly summary for kakaocloud-edu/tutorial. Deliveries focused on improving lab reliability, security, and end-to-end ML workflows in the Kubernetes-based tutorial environment. Key features include updates to Lab11 documentation and image references, Kubernetes lab environment refinements, Docker image path updates, security hardening of user privilege policies, and the addition of a Fashion MNIST Kubeflow notebook, along with MySQL version references updated across documentation. These changes enhance onboarding speed, reproducibility of lab environments, and overall platform security and usability.
March 2026
Activity
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Quality Metrics
Correctness98.2%
Maintainability96.4%
Architecture96.4%
Performance96.4%
AI Usage21.8%
Skills & Technologies
Programming Languages
BashMarkdownPythonSQLYAML
Technical Skills
Database ManagementDevOpsDockerJupyterJupyter NotebookKubeflowKubernetesMySQLPythonSQLSQL scriptingScriptingbash scriptingcloud servicesdata science
Repositories Contributed To
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