This quick tutorial introduces production data sampling within the Deepchecks platform—a powerful way to control evaluation cost and focus on the most meaningful data.
You’ll learn how to define sampling strategies that selectively route data from production into evaluation pipelines, using rules like metadata filters, frequency controls, and randomness.
The video emphasizes how smart sampling helps reduce token and compute consumption while preserving coverage of edge cases and user behavior patterns. It’s particularly useful for teams scaling LLM evaluations without overwhelming budgets or losing sight of critical issues in live environments.
Yaron Friedman
