Data science teams often rely on manual processes to validate code, run tests, and generate reports, leading to inconsistent results and human errors. Without automation, teams forget to run tests before merging, documentation becomes outdated, and data pipelines break silently when dependencies change.
- 01_basic_ci/ - Automatic documentation generation
- 02_data_pipeline/ - Automated data pipeline workflow
- 03_generate_report/ - Automatic report generation
- 04_job_dependencies/ - ML workflow with job ordering
uv sync --group chapter12Automated CI/CD pipelines ensure consistent validation, catch errors early, and enable reliable deployment of data science workflows to production.
← Back to Main README | Next: Chapter 13: Package Your Project →