October 2025: Delivered end-to-end evaluation and testing capabilities, strengthened CI reliability, and cleaned up repository hygiene. Key outcomes span two repositories and focus on measurable business value and technical excellence: - Mean Average Precision (mAP) evaluation tooling and dataset for Module7: introduced a dedicated mAP computation script, integrated with tests, and prepared a predictions.csv data workflow aligned for evaluation of object-detection performance. - Module 8 automated testing pipeline for Exercise 3: added automated testing workflow including target downloads, prediction comparisons, JSON result generation, and management of virtual environments to ensure reproducible tests. - Repository cleanup: removed stray file from Module1 to restore repository hygiene and avoid future confusion. - Test suite hardening for Module7: refinements to test naming, thresholds, and pushed-user handling to stabilize CI results and reduce flakiness. - Cross-repo CI reliability enhancements: incorporated CI reliability improvements from the 2024 project (missing student template handling, robust environment/S3 error handling, and artifact cleanup) to further reduce CI failures and improve integration velocity.

September 2025 monthly summary for racousin/data_science_practice_2025. Focused on establishing a solid project foundation, stabilizing the CI/CD pipeline, and incrementally improving the codebase while enabling new data workflows. Delivered a mix of foundational scaffolding, feature enhancements, and targeted bug fixes that collectively improve developer velocity, code quality, and reliability of automated builds. Key highlights include the following sections below.

March 2025 monthly summary for racousin/data_science_practice_2024: Focused on stabilizing test execution for Module 8 and strengthening repository hygiene to improve reliability and developer productivity.

November 2024 delivered three core features in racousin/data_science_practice_2024 that strengthen testing reliability, automate validation workflows, and improve data governance. The work produced a more stable testing suite, reproducible validation for Module 7/8, and ensured critical data assets are tracked in version control. This accelerates feedback loops, reduces manual debugging, and enhances governance for data assets.