diff --git a/the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco b/the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco
index a08093e..0629fb0 100644
--- a/the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco
+++ b/the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco
@@ -25,7 +25,7 @@ Based on [[https://schlockmercenary.fandom.com/wiki/The_Seventy_Maxims_of_Maxima
 *. Training loss and validation loss should be easier to tell apart.
 *. Any sufficiently advanced algorithm is indistinguishable from a matrix multiplication.
 *. If your model’s failure is covered by the SLA, you didn’t test enough edge cases.
-*. “Fire-and-forget training” is fine, provided you never actually forget to monitor drift.
+*. “Fire-and-forget training” is fine, provided you never actually forget to monitor the run.
 *. Don’t be afraid to be the first to try a random seed.
 *. If the cost of cloud compute is high enough, you might get promoted for shutting down idle instances.
 *. The enemy of my bias is my variance. No more. No less.