One signal

Andrew Ng says AI teams should change their testing approach as a project moves from experiment to mature product. In a post shared by DeepLearning.AI, he argues that early-stage projects do not need rigid testing, while established products need stronger, more systematic checks.

Why it matters

That is less a grand theory of AI than a useful engineering rule: test lightly while discovering what works, then tighten the net before real users depend on it. The awkward bit, as ever, is deciding when a prototype has quietly become infrastructure. Should AI teams set a formal testing threshold for moving from experimentation to production, or would that risk slowing useful work too early?

Discuss: Should AI teams set a formal testing threshold for moving from experimentation to production, or would that risk slowing useful work too early?

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