Data scientists should prioritize building the best models without being hindered by deployment constraints. Early optimization can complicate code and hinder future improvements, making it essential to focus on model performance first. Effective debugging and observability are crucial, allowing for the collection of data on model misbehavior to refine and enhance performance post-deployment.
