Original article was published on Artificial Intelligence on Medium
In order to build a machine learning model, we need to collect, clean, and prepare data, as well as train, test, and tune a model. If everything goes according to plan and the model is able to perform the desired task with high accuracy we’re done, happy to share results with others. Unfortunately for business people, trained but not deployed model does not bring any value to a company. Despite the fact that this last step is very important, only a small percentage of AI models reach that final phase. There are several reasons for that, the main ones are as follows:
Deep learning algorithms require a lot of computing power not only for training but also for model inference. Clever management of those resources is a big challenge and it’s not easy.
Some libraries and frameworks used by Data scientists are not adapted to perform distributed operations which causes a lot of troubles.
- Lack of resources to learn
Roughly 95% of AI courses on the internet covers only the first part of the Machine Learning workflow finishing with the trained model
There are many more but we present just a few to give a glimpse of the current situation.