Building an AI application is much bigger than simply choosing an AI model.
The more I learn and build, the more I realize that the real engineering happens around the model.
I’ve started asking questions like:
→ What data is the system receiving?
→ How should that data be processed?
→ What should AI handle, and what should traditional code handle?
→ How do we validate the output?
→ What happens when the model is wrong?
This has changed the way I look at AI projects.
Instead of thinking:
“Which AI model should I use?”
I’m learning to think:
“What is the best system I can build around the problem?”
That shift in thinking is probably one of the most valuable things I’ve taken away so far from Decoding Data Science.
Still learning, building, testing, and occasionally breaking things along the way. 😅
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