AI/ML Teams: How to Track Research vs Engineering Work
AI/ML teams are unlike any other engineering organization. Half the team is exploring novel approaches where most experiments fail — and that's expected. The other half is building production systems where reliability and speed matter. Many team members do both, switching between Jupyter notebooks and production codebases within the same day. The MLOps maturity model defines this spectrum — from ad hoc experimentation (Level 0) to fully automated ML pipelines (Level 2) — and most organizations sit somewhere in the middle.
Traditional engineering metrics don't capture this duality. Measuring an ML researcher by deployment frequency is like measuring a chef by how fast they wash dishes. But having no metrics at all means you can't tell whether your research investment is producing results or if your production systems are reliable. Papers with Code trend data shows that the gap between state-of-the-art research and production-ready ML is widening — making the research-to-production bridge more critical than ever.
Here's how to build a metrics framework that respects the difference between research and engineering while giving leadership the visibility they need.