- In short
- Change attribution distinguishes the three distinct causes a monitored metric can move for. Model drift is a gradual change in the model behaviour on stable inputs over time. Data drift is a change in the distribution of inputs the system receives, with the model itself unchanged. Model update effects occur when the model version changed and the new version behaves differently on existing inputs. These three require different mitigations, so mixing them up when a metric moves produces the wrong fix.
Full concept guide coming soon
We are building the in-depth, exam-aligned guide for this knowledge point. In the meantime, explore the prerequisites and related concepts below, watch the official Anthropic Academy lessons, and start an adaptive study session to master it with Archie.
Watch and learn
Official Anthropic Academy lessons first, then hand-picked walkthroughs. Videos load only when you press play.
No videos curated for this concept yet
We are still curating the best official and community videos for this topic.
References & primary sources
Adaptive study
Master this concept with Archie
Practice it inside an adaptive study session. Archie, your Socratic AI tutor, tracks your mastery with Bayesian Knowledge Tracing and schedules the perfect next review.