In real-world data environments, perfection is rare. Sometimes a model with 88% accuracy performs better in production than one that hits 95% in the lab.
Would love to hear your approach , what metrics or signals tell you it’s time to deploy? And how do you balance performance with practicality in your ML workflows?
Avatar In my experience, a model is “ready” for production when it’s stable under real-world data shifts, not when it looks perfect in validation.
looking beyond accuracy focusing on precision/recall trade-offs, latency, data drift tolerance, and consistency over time. A model that’s 88% accurate but resilient and explainable is often far more valuable than a fragile 95% one.
Before deployment, also run shadow tests and A/B trials to see how it performs in live traffic. If metrics hold steady and business KPIs show improvement without unexpected behavior, that’s my signal it’s ready.
Perfection is nice in research; reliability and adaptability win in production.
