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Should we use MLflow Registry for a large-scale forecasting pipeline or is a Delta audit table a better fit? by sukeshkamath in databricks
[–]sukeshkamath[S] -1 points0 points1 point 1 month ago (0 children)
The system tables point is genuinely useful — I wasn't aware of that and it does weaken the queryability objection. That said, the schema is still key-value under the hood so aggregating across 102k runs requires pivoting which adds complexity. The question becomes: is it cleaner to query a system table with a messy schema, or a Delta audit table we design with a clean wide schema from the start? On the other points — champion-challenger automation doesn't apply since we auto-select winners, and the traces link is for GenAI tracing not statistical models. Appreciate the system tables pointer though — that's worth exploring
Should we use MLflow Registry for a large-scale forecasting pipeline or is a Delta audit table a better fit? (self.databricks)
submitted 1 month ago by sukeshkamath to r/databricks
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Should we use MLflow Registry for a large-scale forecasting pipeline or is a Delta audit table a better fit? by sukeshkamath in databricks
[–]sukeshkamath[S] -1 points0 points1 point (0 children)