research-pipeline

An ML research pipeline can be managed by enforcing a spec-driven approach that generates spec.md, plan.md, and tasks.md. This structure converts ideas into verifiable artifacts and controlled experiments, reducing wasted compute through governed phase gates.

A spec-driven approach for machine learning experimentation requires generating spec.md, plan.md, and tasks.md before allocating any compute. It automates lifecycle management by enforcing constitutional phase gates and artifact-driven planning for disciplined experiments.

Wasted compute in ML research projects is prevented by enforcing governance rules that require spec.md, plan.md, and tasks.md before any compute is allocated. This risk-aware pipeline ensures effort remains aligned through controlled experiments and phase gates.

Yes, running compute for ML experiments requires specific artifacts. The pipeline enforces governance by requiring a spec.md, plan.md, and tasks.md to be generated beforehand, ensuring disciplined experimentation and clear reproducibility.

The best way to structure reproducible ML research outputs is using an artifact-driven pipeline that generates spec.md, plan.md, and tasks.md. This approach enables clear communication and converts ideas into verifiable artifacts across the project lifecycle.