This project explores the idea that data science workflows can be treated as mathematical objects and optimized algorithmically.
Instead of tuning models within fixed pipelines, the system searches the workflow space to find configurations that balance predictive performance, stability, and computational efficiency.
Planned components:
- Workflow representation engine
- Objective function module
- Optimization algorithm
- Concurrent evaluation system