Modern AI pipelines move fast, but visibility often lags behind. As workloads scale across distributed Ray clusters, teams lose track of which datasets feed which models, which job produced a specific version, and how changes impact downstream systems. Debugging turns into a manual search through logs and tags, and reproducing a past run can stall entire releases. At Ray Summit this week, we announced Lineage Tracking, built on OpenLineage, that maps datasets and models across Ray workloads with native integrations to Unity Catalog, MLflow, and W&B, delivering clearer pipeline transparency, easier debugging, and faster iterations.
Why lineage matters in AI pipelines
How to visualize and trace datasets and models across Workspaces, Jobs, and Services with Lineage Graphs
How to jump into the right workload to debug, then reproduce runs with captured parameters and environment
How to use native integrations with Unity Catalog, MLflow, and W&B
How OpenLineage keeps lineage portable across catalogs and registries
Platform/ML Engineers, data engineers, and MLOps leads running Ray workloads who need reproducibility, auditing, and faster incident/debug loops for their data pipelines and model development workflows.
Reserve your spot today!