We see news about machine learning everywhere. Indeed, there is a lot of potential in machine learning. According to Gartner’s predictions, “Through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization” and Transform 2019 of VentureBeat predicted that 87% of AI projects will never make it into production.

Why is it like that? Why do so many projects fail?

Not Enough Expertise

One of the reasons is that the technology is still new to a large audience. In addition, most of the organizations are still unfamiliar with the software tools and the required hardware.

It seems that today, anyone who has worked in data analytics or software development who has done some sample data science projects are labeling themselves as data scientists after taking a short course online.

The fact is that experienced data scientists are needed to handle most of the machine learning and AI projects, especially when it comes to defining the success criteria, final deployment, and continuous monitoring of the model. 

A Disconnect Between Data Science and Traditional Software Development

A disconnect between Data Science and traditional Software development is another major factor. Traditional software development tends to be more predictable and measurable.

#data science #product development #artificial inteeligence #machinelearning #deeplearning #data strategy