Machine Learning in Production: Lessons from the Field

Getting a model to 90 % accuracy in a Jupyter notebook is the easy part. Deploying it reliably, monitoring it for drift, retraining it on fresh data, and rolling back safely when something goes wrong — that is where most ML projects struggle.

Production ML demands engineering discipline: versioned datasets, reproducible pipelines, feature stores, and model registries. Tools like MLflow, Kubeflow, and Vertex AI Pipelines have matured considerably, but the cultural shift towards treating models as software artefacts is equally important.

Teams that embed ML engineers alongside data scientists from the start ship more reliable systems and spend less time firefighting in production.