AI use cases and applications
Machine Learning
ML models learn patterns from data instead of following hard-coded rules, and they improve as they see more data.
When ML is a good fit
- The rules are too hard to code. Spam filtering depends on many overlapping variables that people can't write down as rules.
- The scale is too big for people. A person can review a few hundred emails, not millions.
When ML is not needed
If you can get the answer with simple rules, computations, or predetermined steps, program it directly. ML adds cost and uncertainty without benefit when the logic is already known.