The base model worked well before.After fine-tuning on new data, accuracy drops everywhere.Even old categories are misclassified.The model seems to have forgotten what it knew.
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Every retraining run produces different artifacts.Code changes, data changes, and hyperparameters change too.Tracking what’s deployed is becoming confusing. Rollbacks are risky?
Test classes that were once simple now require extensive setup and complex assertions. Small changes in automation break multiple tests. Maintaining coverage feels increasingly expensive. I want to understand why this happens and how teams manage it.
transformer model debugging
Our reports were accurate when the org was simple, but over time they’ve started giving inconsistent or confusing results. Multiple formula fields, record types, and automation now influence the same data. Different users also report seeing different numbers. I’m trying ...
I am training a deep network for a regression task.The loss drops initially but then stops changing.Even after many epochs it never improves.The model is clearly underperforming.
A WooCommerce store captures payments normally and customers receive confirmation emails, but some orders never appear in the WordPress Orders screen.The problem started after enabling Redis object caching.Disabling the cache plugin instantly brings the orders back. Why so?!
