Absolutely brilliant breakdown of the infra bottlenck here. The Maya example really hammers home how much time gets burned on data curation instead of actual model improvements. I've seen similiar patterns in manufacturing where teams build bespoke Python scripts for every edge case, and then wonder why their feedback loops are so slow. The insight about RobotOps needing to look forward rather than backward is spot on, especially when validation becomes more gut check than evidence.
It's interesting how you break down the real complexities here. This distinction between MLOps and RobotOps is so crucaial for understanding physical AI deployment. You always manage to articulate these new paradigms so clearly. It realy makes you think about the future of automation. Spot on!
Absolutely brilliant breakdown of the infra bottlenck here. The Maya example really hammers home how much time gets burned on data curation instead of actual model improvements. I've seen similiar patterns in manufacturing where teams build bespoke Python scripts for every edge case, and then wonder why their feedback loops are so slow. The insight about RobotOps needing to look forward rather than backward is spot on, especially when validation becomes more gut check than evidence.
Much appreciated! Glad it struck a chord.
It's interesting how you break down the real complexities here. This distinction between MLOps and RobotOps is so crucaial for understanding physical AI deployment. You always manage to articulate these new paradigms so clearly. It realy makes you think about the future of automation. Spot on!