You wouldn't want to use it for training: This chip can do 4 INT8 TOPs with 2 watts. A Tesla T4 can do 130 INT8 TOPs with 70 watts, and 8.1 FP32 TFLOPs.
Assuming that ratio holds, you'd maybe get 231 GFLOPs for training. The Nvidia GTX 9800 that I bought in 2008 gets 432 GFLOPs according to a quick Google search.
Hobbyists don't care about power efficiency for training, so buy any GPU made in the last 12 years instead, train on your desktop, and transfer the trained model to the board.
On the other hand, it would be useful for people experimenting with low-compute online learning. Also, those types of projects tend to have novel architectures that benefit from the generality of a GPU.
Training is what the cloud is for.
That makes a $170 board that can also do training look dirt cheap in comparison
If you want to train yet-another-convnet sure, but there could be applications where you want to train directly on a robot with live data, as in interactive learning.
See this paper for an example of interactive RL: https://arxiv.org/abs/1807.00412
I'm sure an ML accelerator that doesn't support training will be great for applications like mass-produced self-driving cars. But for hobbyists - the kind of people who care about the difference between a $170 dev board and a $100 dev board - being unable to train is a pretty glaring omission.