TL;DR
Revamped a fragile ML codebase into a clean, testable, and scalable system. Introduced engineering best practices, achieved 86% test coverage, and reduced complexity to 1.67—making the team faster, safer, and more efficient.
As the machine learning team expanded, the codebase became increasingly difficult to manage. It had grown rapidly without a strong architectural foundation, leading to low test coverage, inconsistent structure, and high friction when onboarding new contributors or scaling the system.
I took on the responsibility of improving the overall quality, maintainability, and sustainability of the ML team’s codebase. The objective was to introduce best practices that would increase code reliability and engineering efficiency without slowing down experimentation or delivery.
I led a full-scale engineering overhaul. This involved introducing object-oriented programming principles to improve structure, shifting the team toward test-driven development, and setting up proper code review workflows. I built out a comprehensive suite of automatic testing using pytest, implemented static code analysis with radon to monitor complexity, and enforced consistency with linting. I containerized the environment for reproducibility, modularized the application for scalability, and introduced a branching strategy to streamline collaboration. Legacy sections of the code were refactored and reusable templates were introduced to guide new development.
These changes raised the engineering maturity of the team significantly. We achieved 86% test coverage (up from 0.34%) and brought cyclomatic complexity down to 1.67 (from 7.53), drastically improving code reliability and reducing the likelihood of regression.
| Metric | Before | After |
|---|---|---|
| Test Coverage | 0.34% | 86% |
| Cyclomatic Complexity | 7.53 | 1.67 |
The ML applications became easier to debug, extend, and deploy—setting a sustainable foundation for future growth.