AI Dev Skills
Tools for managing the ML lifecycle — experiment tracking, data versioning, pipeline orchestration, and model registry. The engineering infrastructure behind reliable AI development.
Reproducibility and iteration speed determine how fast a team can improve their AI product. Without MLOps, teams waste time on "what did we try?" and "why did this model break?"
MLflow is the standard open source experiment tracker. DVC for data versioning. Ray for distributed training. The MLOps stack has stabilized — most teams use 3-4 tools from this category.
3+ MLOps repos signals engineering discipline around AI development. These teams can reproduce any experiment, track data lineage, and reliably ship model updates.
No repos in this skill area yet.