What is happening to Data Science projects.

"We want to use Data Science whatever we're doing/developing. But we need to be wise and use the right tools to do so."

The history of Data Science, more specifically Data Analysis, goes back to 1962, when John W. Tukey writes in his academic essay “The future of Data Analysis”: “data analysis is intrinsically an empirical science…”. Since then, it evolved in the speed everything else in the technology world evolved – exponentially faster along the years.

Now it is hard to find what is the company that doesn’t want to use Machine Learning, Artificial Intelligence, Data Analytics – all of the terms under the greater umbrella “Data Science”, really – to improve its outcomes and thrive in the contemporary world.

With this goal, a lot of businesses have rushed into this marvellous science of Artificial Intelligence, but they couldn’t make it properly and, according to Gartner’s 2021 trends eBook, they are most likely to fail “due to issues with maintainability, scalability and governance”.

This wouldn’t happen with a robust AI engineering, though. The AI engineering, as Gartner, 2021 says, stands upon three pillars: DevOps, DataOps and MLOps.

DevOps deals with high-speed code changes; DataOps is a variety of practices that enable several improvements in the development of data projects; MLOps are simply the best practices for businesses to run AI and have the best outcomes from it.

We want to use Data Science to be better in whatever we are doing/making/developing. But we must be wise, and use the right tools for it. Learn more here: https://qintess.com/en/qads