The latest articles on DEV Community by aarnav preeth (@aarnav_preeth_3f78c31815f). https://dev.to/aarnav_preeth_3f78c31815f https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3763818%2F0438968b-f1c5-4f06-9ca0-d932c45b5ccd.png https://dev.to/aarnav_preeth_3f78c31815f en aarnav preeth Tue, 10 Feb 2026 09:00:49 +0000 https://dev.to/aarnav_preeth_3f78c31815f/pre-deployment-evaluation-for-models-that-run-continuously-36ne https://dev.to/aarnav_preeth_3f78c31815f/pre-deployment-evaluation-for-models-that-run-continuously-36ne <p>When working with models that run continuously, I’ve found it hard to reason about how performance degrades over time using only static train/test evaluation. For those of you who deploy long-lived models: how do you currently build intuition about model behavior under distributional change before deployment, if at all? What kinds of tools or practices do you rely on?</p> datascience discuss machinelearning testing