A few examples I can think of off the top of my head: - AirBnB uses a model to recommend a daily price for a new listing. They have to base this off the area a

A few examples I can think of off the top of my head: - AirBnB uses a model to recommend a daily price for a new listing. They have to base this off the area and how other similar listings are priced. - Uber has an entire team (maybe teams) dedicated to forecasting demand at a location/time, they distribute this information to divers so they can plan to be certain places at certain times to get more fares. - Uber collects your location 5 minutes before/after your ride. They use this (at least in part) to figure out where people normally go when they request to be dropped off at an address, and then they can optimize the drop-off location using that information. Really though, the trick is to find a problem that can be solved with machine learning, and where profit of the solution is multiple times the cost of developing it. The only way to spot these opportunities reliably is to have a lot of machine learning experience, and quite a bit of domain knowledge. It may be worth following some tech company ML engineering blogs to get a better grasp of how they leverage ML. [1]: https://medium.com/airbnb-engineering/aerosolve-machine-learning-for-humans-55efcf602665 https://medium.com/airbnb-engineering/aerosolve-machine-lear... [2]: https://eng.uber.com/forecasting-introduction/ https://eng.uber.com/forecasting-introduction/ [3]: https://www.npr.org/sections/alltechconsidered/2016/12/01/503985473/uber-now-tracks-passengers-locations-even-after-theyre-dropped-off https://www.npr.org/sections/alltechconsidered/2016/12/01/50...