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Taking on an artificial intelligence project requires a very specialized team. However, finding skilled talent is a problem that needs to be addressed, as there are less than 10,000 qualified individuals with the necessary capabilities in the world, as the NYTimes points out. Every year, only about 100 new candidates are produced with the skills needed to take on an AI project.
What are these roles that need to be filled?
A machine learning project needs machine learning engineers at the heart of the team. They manage not only the operations of the project but also the required infrastructure and data pipelines. They are typically skilled in data science, applied research, and coding. eBay VP of Engineering Japjit Tulsi claims that they "straddle the line between knowing the mathematics and coding the mathematics."
With backgrounds in data science, statistics, computer science, math or physics, data scientists focus on the analysis of data -- from collecting to cleaning and preparing data -- as well as developing algorithms.
Data leads that this particular set of scientists discover are then built on by research scientists who are concerned with scientific discovery or approaches.
Applied research scientists are knowledgeable in both data science and computer science. Not only could they tackle the data science of an AI project, they are also able to code.
Equally crucial to a project are the distributed systems engineers. With an in-depth understanding of these systems, they are equipped to manage issues involving large data sets and to operate distributed systems.
The growing demand for artificial intelligence solutions likewise increases the demand for a qualified team of AI specialists. With a shortage of skilled candidates, building the right team continues to pose a major challenge in the field.