← Applied ML System Design Specialization
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Framing ML Product Problems
Translate business goals into measurable prediction tasks.
Framing ML Product Problems
Not every problem needs a model. Start with the decision, the cost of errors, and the data you can actually get.
Design doc essentials
- User decision & frequency
- Labels / feedback loops
- Baseline non-ML solution
- Success metrics and constraints (latency, privacy)
- Failure modes and human fallback
If you cannot define labels and costs, you are not ready to train.