My AI Engineering Internship Journey: Lessons Learned Building Industrial ML Pipelines

Moving from academic machine learning to production AI engineering requires a fundamental shift: accuracy scores mean nothing if inference latency exceeds SLAs or data distributions drift over time.

"In production, ML code is only 15% of the codebase. The remaining 85% is data verification, feature store pipelines, telemetry logging, and model serving infrastructure."