I’m trying to understand how real-world ML/AI products are built in production, especially from a team perspective.
Initially, I thought the main challenge was choosing the right model or improving accuracy. But in production, the bigger challenge seems to be the team building the end-to-end system.
In practice, ML systems involve much more than training models:
data pipelines
deployment and inference systems
monitoring and retraining
scaling with real user data
Because of this, companies often struggle to decide whether to hire generative AI engineers, work with agencies, or build in-house teams.
What’s hard is that most candidates look strong on paper, but production readiness only becomes clear after deployment.
So, I want to know
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