The right approach to MLOps is the key: Effective data orchestration to cultivate high quality data, managing the risk of poor model fit, and automating robust systems to avoid disconnected pipelines reduces the risk of your team’s projects succumbing to these common pitfalls that unnecessarily bloat project timelines.

We need to get a lot more precise than when talking about data in the everyday world.  Code is rigorous because we describe it precisely. We don't have to settle for asking, "What does the code do?" we can ask "What does this function do?" or even "What does this line do?" If we want rigor for data, we must first describe it rigorously.