Building Data Products That Actually Ship

The biggest risk in data-heavy projects is not model quality. It is delivery drift. Teams spend weeks tuning analyses without locking what production success looks like.

I now start with product constraints first: who needs the output, what latency is acceptable, and which failure mode is tolerated. Once those are fixed, architecture and modeling choices become much simpler.

For most teams, a good baseline pipeline with deterministic inputs, strong observability, and explicit ownership beats a complex system that no one can maintain. Reliability compounds faster than novelty.

If a feature cannot be monitored, rolled back, and explained to non-technical stakeholders, it is not ready. Shipping is an operational discipline, not just a modeling milestone.