The ML Surprise | Decision Intelligence News | Scoop.it
When I talk to practitioners that have had a lot of experience building and scaling these ML systems inside Google, I hear a very different story. Based on these conversations, optimizing an ML algorithm takes much less relative effort, but collecting data, building infrastructure, and integration each take much more work. The differences between expectations and reality are profound.