What Makes Deep Learning Not the Right Fit 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.
What Makes Deep Learning Not the Right Fit?
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.
Limited Interpretability of Deep Learning
Deep neural networks are known for being these black boxes whose inner operations are not really interpretable. It’s not that we’re not trying to understand it. There is some work in this area but as of yet, no general answers. This ability to explain solutions is inherent to many simpler methods, in particular, linear ones, where the direct relationship between parameters can be analyzed.
Why is this interpretability important? From the business owner’s point of view, interpretability is important because it can give new insights into relationships between numerous variables and expected outcomes. It is also great to prove that the model does not operate magically which increases the trust of the people who use it in their daily business decisions.
Conclusion
For every success story, there are thousands of cases of companies bogged down in a deep AI solution they had no business getting into in the first place. The hype surrounding AI and deep learning is not unjustified – it’s a game-changer in many industries. Right on this blog, we’ve covered how deep neural networks are used in accounting and medicine. That does not mean that it’s the right choice for you. Make sure you’re fully aware of what deep learning solutions entail before you embark on that journey, and remember that most often the best solution is the simplest solution that works.
FAQs
- What is deep learning?
Deep learning is a subset of machine learning with several unique characteristics that can be a complete deal-breaker when choosing the right solution for your business. - Is deep learning always the best choice?
No, not every company lives on the cutting-edge of innovation, and many problems do not require very complex and sophisticated methods which only increase costs and time. - What are the limitations of deep learning?
Deep learning demands huge sample sizes to train on, is inherently slow, and lacks interpretability, making it not the right choice for many businesses.
