GDPR was supposed to be a burden. It made my systems better. When GDPR hit, most companies panicked. I saw it differently—as forced discipline to clean up years of sloppy data practices. Turns out, when you can't hoard unnecessary data, you build smarter systems. 📊 The right to explanation forced us to design transparent AI. The right to deletion forced us to architect with data lifecycle management. The consent requirements forced us to respect user agency. Every "restricti

Accuracy is a lab metric. Production needs better. I've shipped enough AI systems to know this truth: a model can be 98% accurate in testing and still fail spectacularly in production. Why? Because accuracy doesn't capture reliability, explainability, or business impact. 📊 The metric I actually trust? Time to resolution for errors. How fast can the system detect when it's wrong, route to human oversight, and learn from the correction? That tells me everything about operation