Why deep learning may be not a good idea?
We may know the power of deep learning in AI/data science world. In this post, I would love to share some experience in working with deep learning (which has unfortunately not been applied in production due to the drawbacks):
1. Deep learning is computationally expensive:
I once was asked to model a time-series regression with a deep neural network.
After trained in the remote host for a couple of days, the testing on new coming data has been unsuccessfully passed as the model loading consumes a significant amount of time.
Yes, we already paid quite a lot for using a remote host, but in the end, the deep learning model can’t be in production.
The training process also requires a huge amount of data. This means if we want to improve the model performance, +GBs (or even +TBs) data should be a must. Unluckily, the more data are trained, the longer the training is. (Hence, more money will be charged)
Basically, deep learning is not a good idea (an over-investment) for small-medium enterprises or startups.
2. Deep learning is a black box:
Sometimes, I may even do not know why deep neural net perform well in this case but does not in another case. I faced an issue while the model performance is degraded when new data have a (slightly) different distribution compared with the training data.
Also, deep learning model contains multiple layers. The more (layer) we add, the harder we track how data are being processed unless I monitor and visualize each layer. The relationship between input and output is quite “invisible”.
Of course, there is another to open this blackbox by self-building the deep learning model from scratch (instead of using frameworks like kearas/tensorflow). However, this requires some deep technical/programming knowledge.
By: Linh Viet Nguyen