I trained a model that performed really well during experimentation and validation.The metrics looked solid, and nothing seemed off in the notebook.However, once deployed, predictions started becoming unreliable within days.I’m struggling to understand why production behavior is ...
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As more features are added, previously stable tests begin failing. These failures seem unrelated to recent changes. Maintaining tests now takes significant effort. I want to understand why this happens.
image failure on alpine
Unit tests don’t catch ML failures.Integration tests are slow.Edge cases slip through.I need better confidence.
Models are trained successfully.Deployment feels rushed.Problems surface late.The team loses momentum.
Errors often state something failed without explaining why. Users struggle to act on these messages. I want to understand why Salesforce errors are often unclear.
