AI Dev Skills
MLOps & Data
What is it?
Tools for managing the ML lifecycle — experiment tracking, data versioning, pipeline orchestration, and model registry. The engineering infrastructure behind reliable AI development.
Why it matters for AI PMs
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?"
The 2026 landscape
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.
What strong coverage looks like
3+ MLOps repos signals engineering discipline around AI development. These teams can reproduce any experiment, track data lineage, and reliably ship model updates.
Your library coverage (0 repos)
No repos in this skill area yet.
Key concepts to know
- •Experiment tracking and reproducibility
- •Model registry and versioning
- •Data versioning with DVC or Delta Lake
- •Pipeline orchestration (Airflow, Prefect, ZenML)
- •Feature stores for training/serving consistency