Machine Learning Developers: Why Most ML Projects Fail After the Model Stage

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 Actual