Ten Costly Mistakes

Every experienced practitioner has scars. These next ten mistakes have cost companies millions of dollars. Learn them here, for free, so that you never have to learn them the hard way. See how many you remember from earlier in the course.

• Every practitioner has scars
• These cost millions
• Learn them here for free

The first five

Number one, solving the wrong problem, with a beautiful model that nobody needed. Number two, skipping the baseline, so that you can't tell whether your model helps. Number three, data leakage, which gives you amazing fake scores. Number four, testing on data that the model has seen. Number five, using accuracy on rare events. All five of these lead to the same place, a model that looks great, right up until it meets the real world.

The second five

Number six, ignoring how the data was made, so that hidden bias gets baked in. Number seven, overtuning, spending weeks on a tiny gain when better data would do far more. Number eight, building something too complex to maintain. Number nine, never monitoring after launch. And number ten, failing to explain the results to the people who must act on them. The best model in the world is worthless if nobody trusts it.

A short checklist

Before you ship anything, ask five questions. What decision will this change? What does the simplest baseline score? Could anything be leaking? How will it fail, and who gets hurt when it does? And how will we know if it stops working? If you can answer all five, you're ahead of most teams.

Summary