Why deep learning may not be the right solution for your business
Deep learning has been all the rage. From chatbots in customer service, through image recognition solutions in retail, to autonomous vehicles in transportation — artificial intelligence companies seem to be shaping the future of business. But, like with every tech craze, confusion and overblown expectations reign supreme, and like moths to a flame, way too many businesses reach for deep learning solutions when they shouldn’t.
There are several factors that make relatively simpler models more suitable than their deep learning counterparts , but first, let’s quickly address what deep learning really is. Unfortunately, too often, deep learning is used interchangeably with AI and machine learning (ML) which is not the case at all. To get a more clear understanding of AI and ML, check out our Everything you need to know about… post.
Deep learning is a subset of machine learning (which is a subset of AI) with several unique characteristics that can be a complete deal breaker when choosing the right solution for your business. This post is about these characteristics.
What makes deep learning not the right fit for your business?
COSTS
Yes, deep learning developments bring about monumental breakthroughs, but not every company lives on the cutting-edge of innovation. The problems most, especially small, businesses are facing do not really require very complex and sophisticated methods which only increase costs and time.
The costs of developing ML solutions are not cheap to begin with, and then they only skyrocket with increased complexity.
Why?
There are many more decisions to make and test. They include choosing the right type of network architecture, activation functions, optimizer, regularization strategy, the list goes on, not to mention a lot of hyperparameters that also need to be fine-tuned.
Deep learning is also inherently slow. For example, several weeks of just training using multiple GPUs is nothing extraordinary. Having to make all these decisions also means deciding to pay for them as more employee time and stronger machines are required.
NOT ENOUGH GOOD-QUALITY DATA
Many businesses are just now starting to catch up to the information revolution and begin understanding the value of storing data. That means that despite their good intentions and current enlightenment, their data sets are not big enough for deep learning.
Let’s remember that deep learning demands huge sample sizes to train on. Deep neural networks often take hundreds of thousands or even more samples to achieve high performance.
Of course, there are some areas of application where complex models can be used even without huge datasets. For example, it is sometimes possible to use already pre-trained models as the basis of our own solution, but these areas are very limited. Currently, such techniques are only applicable to some types of image classification (like detection and identification of animals, cars, boats, etc.) and some limited NLP (Natural Language Processing).
Also, many problems require so-called labeled datasets (meaning that each sample is annotated with an expected value, e.g., a classification result or output value to be predicted). Such labeling is time-consuming and may often require hiring people to do it manually — read more costs.
Originally published at dlabs.pl on March 8, 2019.
