Training a model is easy. Getting 85–90% accuracy in a notebook? Also doable. But getting that model to run reliably in production and drive real outcomes? That’s where most teams fail. The Real Gap: Model vs System A trained model ≠ a working ML system. And this is exactly where machine learning developers come in. They don’t just build models. They build systems that: Ingest data continuously Serve predictions in real time Integrate with applications Improve over time What ML Developers Actually Work On If you’re building anything serious, expect these layers. Data Pipeline (Everything starts here) Before modeling: Data ingestion (batch/stream) Cleaning & normalization Feature engineering Storage (data lake / warehouse) Tools: Pandas, Spark Airflow / Prefect Kafka (for streaming) Bad pipeline → unstable system.…