How do you decide whether machine learning is the right solution for a problem?

Interviewers value people who say no to ML, because unnecessary models are expensive and fragile.

ML is appropriate when:

• The pattern is genuinely complex and cannot be expressed as rules a human would write.
• You have enough labelled, relevant historical data — and the future will resemble it.
• Errors are tolerable and their cost is understood. A model that is wrong 10% of the time is fine for recommendations and unacceptable for some safety decisions.
• The decision is repeated often enough to justify building and maintaining a system.

ML is the wrong choice when:

• A rule would work. If a domain expert can write down the logic, write it down. It is cheaper, explainable, and does not drift.
• You lack labels, and acquiring them is impractical.
• The decision must be fully explainable for regulatory reasons and a simple model will not suffice.
• Nobody has committed to acting on the output.

Note: Always propose a baseline first — a heuristic, a simple rule, or a logistic regression. It sets the bar, it is often good enough, and if the sophisticated model cannot beat it, that is a finding rather than a failure.