Echoing Dr. Ian Malcom’s wisdom from Jurassic Park, the rapid growth of generative AI mirrors the risks of unchecked ambition. As hardware struggles to keep up with demands, concerns extend beyond fictional scenarios like SkyNet to the tangible toll of electricity and water consumption.

The data science (DS), machine learning (ML), and artificial intelligence (AI) field is adapting and expanding. From the ubiquity of data science in driving business insights, to AI’s facial recognition and autonomous vehicles, data is fast becoming the currency of this century. This post will help you to learn more about this data and the profile of the developers who work with it.

In this blog post we’ll explore where ML developers run their app or project’s code, and how it differs based on how they are involved in machine learning/AI, what they’re using it for, as well as which algorithms and frameworks they’re using.

In recent years artificial intelligence (AI) has returned to the forefront of technological debate. That debate has moved on from when, and even whether, computers will ever display intelligent behaviour to how smart they will get, how quickly, and what the implications are for society. Although there are multiple approaches to creating AIs, the ones that involve machine learning from large datasets are generally outperforming all others.