You can't predict every surprise, but you can prepare for them. ✍🏻 The goal in the age of AI isn't perfection—it's resilience. By understanding the "knowns and unknowns" framework, you can design for failure and contain the blast radius of a bad model. This is how data engineers turn reaction into lasting progress and build trustworthy systems. Learn more 👉🏻 https://bit.ly/3XiziFW
Snowflake🎯 The shift from "perfection" to building resilience is the core paradigm of modern data strategy. I believe designing for failure is paramount for mitigating model risk and maximizing long-term business value. Excellent insight! ✅ #snowflake #DataAnalytics #AI #MLOps #ML
The real differentiator is the ability to contain failure, shorten feedback loops and build models that degrade gracefully when reality shifts. Teams that design around "knowns and unknowns" end up shipping AI that earns trust, not just benchmarks.
Solid point, resilience comes from designing for the unknowns, not pretending they don’t exist.