Key features: - 64+ automated tests (pytest) - ML anomaly detection (Isolation Forest, 200 estimators) - Pydantic validation for data integrity - Docker + CI/CD ready - MIT License
Tech stack: Python 3.12, scikit-learn, Pydantic, GitLab CI
Happy to answer questions about the architecture or ML approach! Also looking for feedback on what features would make this production-ready for Tesla/Rivian/Lucid scale.
Currently seeking QA/ML Engineer roles in the EV industry - this project showcases my testing + ML skills.