[Advice Wanted] How do you handle model drift over time?

Comments

• Have you tried using evaluation datasets to continuously test your models? We maintain a set of "golden examples" that new model versions must pass before deployment.
  By: User #12
• Vector embeddings are definitely the way to go. We've had good results with ChromaDB for storing our embeddings - it's lightweight and easy to integrate.
  By: User #9
• We've found that having a robust monitoring system is more important than frequent retraining. We only update our fine-tuning when we see consistent degradation.
  By: User #11
• Has anyone tried using vector embeddings to detect when the semantic meaning of outputs starts to drift? We're exploring this approach currently.
  By: User #12
• We monitor for model drift by keeping a holdout set of prompts and expected responses, then running weekly evaluation jobs to track performance changes.
  By: User #23