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I don't know what qualifies as novel for you but some use cases I've seen: On the retail side: Using computer vision to deliver alerts about shelf condition.
by brd 9y ago
I don't know what qualifies as novel for you but some use cases I've seen:
On the retail side: Using computer vision to deliver alerts about shelf condition.
For farming: Using computer vision + ML to devise and track health monitoring for crops.
For manufacturing: Predictive maintenance of equipment has been a very popular area of focus.
There have been countless use cases on the finance side of things. For instance, anomaly detection techniques help with reconciling accounts and detecting fraud.
The energy industry seems to never run out of use cases for tracking commodities and/or helping predict load.
In HR, predicting turnover and education demands are some of the early use cases being approached but I expect a lot more over time.
Logistics is another area that will have a seemingly endless supply of use case. Things like loss tracking, warehouse optimization, raw material allocation and sourcing. I don't think I've ever been involved in a logistics/manufacturing project that couldn't have used some ML to add efficiency to the process.
- riku_iki 9y agoI am curious if DL really can deliver good results in such spaces. We all see success stories for very refined and well defined problems with huge amount of training data, with models created by 1% top engineers, but for average business such conditions may not be achievable, to train model to recognize various shelf conditions in different situations, buildings, etc. you need nontrivial set of training data, and will have unclear expectations about model performance.
- cstejerean 9y agoMost businesses will probably not develop and train their own systems, but rather implemented solutions developed by the folks with the expertise and training data.