The Five Levels of Machine Learning Use Cases

Level 0: Talk to users

Level 1: Identification (sometimes called Classification)

  • I took a picture. Machine learning tells me it’s a dog.
  • I received an email. Machine learning tells me it’s about dog food.
  • I am on a website. Machine learning tells me it has a shopping plug-in.
  • I am looking at a bank statement. Machine learning tells me there is a grocery transaction.

Level 2: Categorization

  • I have 1,000 pictures. Separate the dog and cat photos.
  • I have 10,000 emails. Group them into hierarchies: which ones are about pets, and of those, which ones are about dog food vs cat food.
  • I have 100,000 websites. Categorize them by brand tone.
  • I have 1M bank statements. Label each transaction as Retail, Services, or Other.

Level 3: Assessment

  • I have 1,000 pictures. Tell me which ones have a sick dog.
  • I have 10,000 emails. Tell me which ones are high priority.
  • I have 100,000 websites. Tell me which ones have illegal terms and conditions for their respective countries.
  • I have 1B banking transactions. Tell me which ones are fraudulent.

Level 4: Recommendation

  • I think I have a picture of sick dog. Now what?
  • I think I have an important email. Now what?
  • I think I have an illegal website. Now what?
  • I think I have a fraudulent bank transaction. Now what?

Level 5: Prediction

  • Will my vet clinic see more sick dogs next month?
  • Which customers are most likely to purchase a couch in the next 60 days?
  • Can I predict a fraudulent bank transaction? How should I best notify the user/freeze the card?
  • Is a power outage likely to happen in San Francisco this month? How can we mitigate that?

Ascending the Ladder: Final Thoughts

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Global Head of Machine Learning BD, Startups and Venture Capital, AWS. Proud Wharton and Dartmouth alum. Champion axe thrower. Views are my own.

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Allie Miller

Allie Miller

Global Head of Machine Learning BD, Startups and Venture Capital, AWS. Proud Wharton and Dartmouth alum. Champion axe thrower. Views are my own.

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