Docker-Based-Deployment-for-ML-on-High-Entropy-Alloys jeffwongqy · COMPLETED
This A*STAR project developed a machine learning pipeline to predict HEA yield strength, ultimate tensile strength, and elongation, incorporating feature selection, optimisation, evaluation, model serialisation, and Dockerization.
github.com/jeffwongqy/Docker-Based-Deployment-for-ML-on-High-Entropy-Alloys · ★ 0 · Forks 0 · Size 23.8 MB
SUMMARY
Technologies 6
Scored 6
Observed 6
Practices 6
Evidence 7
Skips 0
COVERAGE
Analyzed 15 files · 37 commits · 0 API calls
TECHNOLOGIES & DEPTH
Dockerfile LANGUAGE Depth 70
1 files · PRODUCTION
Markdown LANGUAGE Depth 70
1 files · PRODUCTION
Python LANGUAGE Depth 70
2 files · PRODUCTION
pip BUILD_TOOL Depth 80
1 files · CONFIGURATION
NumPy LIBRARY Depth 54
1 files · PRODUCTION
pandas LIBRARY Depth 58
2 files · PRODUCTION
PRACTICES
documentation · observedautomated_tests · absentcontinuous_integration · absentcontainerization · observedlinting · absentformatting · absent
ACTIVITY & OWNERSHIP
First commit 2025-07-25
Last commit 2026-09-01
Active months 5
Commits 37