just stumbled onto a decent breakdown of how to stop ml models from dying in production. most projects fail because of training-serving skew, where features at inference dont match what was used during training. it is basically the
silent killer of all ml systems . using
Spark Structured Streaming and
Databricks Feature Store seems like the way to handle real-time engineering w/o the headache.
>it is not about the model being wrong, but the data being inconsistent. anyone else found better ways to keep features synced in real-time?article:
https://dzone.com/articles/real-time-ai-features-spark-databricks