Running AI-generated cold outreach at scale and paranoid about what's slipping through unseen. Currently manually spot-checking a sample before sending but it doesn't scale. Curious what others are doing — any systems, tools, or workflows for catching AI mistakes before they hit prospects?
Running AI-generated cold outreach at scale and paranoid about what's slipping through unseen. Currently manually spot-checking a sample before sending but it doesn't scale. Curious what others are doing — any systems, tools, or workflows for catching AI mistakes before they hit prospects? Genuine question, not promoting anything.
Hey all. We're dealing with a nightmare right now where upstream software/data engineering teams keep making subtle schema changes (dropping columns, changing unit types, renaming API fields).
The traditional ETL/dbt tests all pass because the data pipelines themselves don't technically "break." But the feature pipelines ingest that skewed data, and our downstream ML models (specifically credit/fraud) just silently rot in production. We don't realize the model's predictions have degraded until days later.
It feels like there’s a massive gap between the data warehouse and the feature store. Great Expectations feels too heavy and slow for this, and generic pipeline monitoring doesn't catch the ML-specific context.
How are your teams handling data contracts or putting circuit breakers in place before the data hits the models? Is anyone actually doing this well, or is everyone just manually firefighting feature drift?