It is quite usual to talk about Artificial Intelligence on a very technical and academic level (and we have already considered this approach in many previous posts). And it is also quite common to read posts and website pages in which companies tell the world that they deliver “ML-based solutions”. But is it actually true? Do you really know what happens when Machine Learning models need to concretely face market’s and customers’ needs?
As mentioned before, often AI is not taken into account from a business-centered point of view, even if many companies use “AI” as a magic word when they talk about their products. Actually this kind of technology needs to be approached in a specific way to really create effective solutions for the market.
Without going too deep in ML-Ops related issues, the image shown above is a crystal clear example of this field’s complexity. A Machine Learning model can be very accurate in distinguishing between pictures of wolves and pictures of huskies. But, what if its accuracy was based on the snow in the background? Would you rely on a system that is not based on the awareness about its sector of application and its starting needs? In that case, the problem is not taking into account the huge difference between correlation and causality, and it often happens that these two concepts are erroneously overlapped. But this issue becomes even bigger when Artificial Intelligence is used for real solutions, stepping out of the academia research.
At Aptus.AI we have clear the complexity of developing effective AI solutions for the real world, and not just functioning models. This is why we have created - and we follow - a tailored and tested approach, which has its roots in a deep knowledge of Machine Learning and mostly in a clear awareness of the difficulties that developing ML-based products presents.
The latter can substantially be divided into two macro-types, that are:
Starting from the first kind of issues, we can identify three further levels of the problems depending on the product itself, namely:
Some lines above we have talked about a “tailored and tested approach”, and it was not just an expression. At Aptus.AI we have implemented a specific workflow which goes beyond the normal steps of model design and offline evaluation used in the academia world to develop ML systems. And this is how we have created Daitomic, our AI compliance management platform addressed to the RegTech market. What are the further steps that we have added? Keep following us to discover them - starting from next post!