Most AI/ML tutorials stop at training a model.
Real systems start after that.
In production, the hardest problems are not modeling — they are:
Data quality drift
Evaluation reliability
The Golden Pipeline for AI/ML Systems in Production
Most AI/ML tutorials stop at training a model. Real systems start after that. In production, the...
TL;DRAI
Golden Pipeline: data validation → training → evaluation → registry → shadow/canary deployment → monitoring → feedback. Most AI failures are data pipeline failures, not model failures. Tech managers should treat the pipeline—not the model—as the product: data lineage, canary deployments, rollback, monitoring. Design systems for failure.
622 words~3 min read
