ML Monitor

Where ML Fails (and Why Monitoring Matters)

Most ML failures don’t happen in notebooks.
They happen after deployment — when drift sets in, pipelines break, or no one’s watching.

ML is shifting:
→ From experimenting → to shipping
→ From metrics → to impact

A 3% CTR is worth more than a 0.5% AUC lift.
Business wins in days > model wins in months.

Infra matters. Monitoring is a must.
(That’s where tools like W&B come in.)

The real challenge isn’t building better models.
It’s keeping them alive and accountable — at scale.