Why Your ML Pipeline Is Breaking in Production—And How to Fix It
Machine learning prototypes like a dream and deploys like a nightmare. If we ask any team that’s scaled an ML project beyond a notebook, and they’ll tell you: getting a model to work is the easy part. Keeping it working—correctly, reliably, and ethically—in production? That’s where the real battle begins.