building an impressive ai prototype is one thing ⚡ but making it run smoothly in real life? totally different ballgame . i just stumbled upon this article on why many cool demos fail to deliver once theyre out of the sandbox.
basically, there are three main culprits:
1️⃣
data drift : your model might work great initially but lose its edge over time as new data comes in.
2️
scalability issues: it runs like a champ on that fancy gpu cluster during demos. how does performance hold up when everyone starts relying heavily?
3️⹂testing gaps*: thorough testing before launch is key, yet many rush through to get the demo done.
anyone else had projects struggle post-demo? what lessons did you learn?
thoughts anyone?https://thenewstack.io/ai-demo-to-production/