team-implant/imPlant
May 2025 – May 2025
1 Month active
Languages Used
BashC#DockerfilePythonShellYAML
Technical Skills
.NET.NETARM64Backend DevelopmentCI/CDContainerization
SebinoBR
PROFILE
Sebinobr
Worked on the team-implant/imPlant repository to deliver a multi-architecture deployment pipeline and modernize CI/CD processes. Developed architecture-specific Dockerfiles and automated build and push steps for AMD64 and ARM64, publishing images to GHCR for scalable, reliable deployments. Enhanced workflow resilience by refining image refresh strategies and addressing packaging issues to improve release stability. Built data-driven water pump status prediction models using Python, Pandas, and Scikit-learn, automating data retrieval, model training, and visualization in a notebook. These efforts reduced deployment friction, increased production readiness, and enabled predictive maintenance through actionable machine learning insights and robust DevOps practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
19Total
Bugs
0
Commits
19
Features
2
Lines of code
1,011
Activity Months1
Your Network
8 peopleWork History
May 2025
19 Commits • 2 Features
May 1, 2025May 2025 performance summary for team-implant/imPlant: Delivered a robust multi-architecture deployment and CI/CD modernization, introducing AMD64/ARM64 containerization with architecture-specific Dockerfiles, isolated build/push steps, and GHCR publishing to enable reliable, scalable deployments. Implemented data-driven water pump status prediction models (logistic regression and random forest) with end-to-end data fetching, model training, and a visualization notebook to support predictive maintenance. Addressed key workflow reliability issues and packaging fixes to improve release velocity, reproducibility, and maintainability. Overall, these efforts reduced deployment toil, improved production readiness, and added data-informed maintenance capabilities.
19 Commits • 2 Features
May 1, 2025May 2025 performance summary for team-implant/imPlant: Delivered a robust multi-architecture deployment and CI/CD modernization, introducing AMD64/ARM64 containerization with architecture-specific Dockerfiles, isolated build/push steps, and GHCR publishing to enable reliable, scalable deployments. Implemented data-driven water pump status prediction models (logistic regression and random forest) with end-to-end data fetching, model training, and a visualization notebook to support predictive maintenance. Addressed key workflow reliability issues and packaging fixes to improve release velocity, reproducibility, and maintainability. Overall, these efforts reduced deployment toil, improved production readiness, and added data-informed maintenance capabilities.
May 2025
Activity
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Quality Metrics
Correctness82.0%
Maintainability83.2%
Architecture82.0%
Performance74.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
BashC#DockerfilePythonShellYAML
Technical Skills
.NET.NETARM64Backend DevelopmentCI/CDContainerizationData ScienceDockerDocker ComposeGitHub ActionsMachine LearningMulti-arch buildsMulti-architecture buildsPandasPython
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline