Sam Anusha

Asked: January 6, 2026In: MLOps

How should I version models when code, data, and parameters all change?

Every retraining run produces different artifacts.Code changes, data changes, and hyperparameters change too.Tracking what’s deployed is becoming confusing. Rollbacks are risky?

Asked: January 1, 2026In: MLOps

What’s the biggest mistake teams make when moving ML to production?

Sam Anusha

Models are trained successfully.Deployment feels rushed.Problems surface late.The team loses momentum.

Asked: September 11, 2025In: MLOps

Why does my ML model show great accuracy during training but fail after deployment?

Sam Anusha

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 ...

Asked: August 19, 2025In: MLOps

Why do my experiment results look inconsistent across runs?

Sam Anusha

I rerun the same experiment multiple times.Metrics fluctuate even with identical settings.This makes comparisons unreliable.I’m not sure what to trust.