A new report has revealed the critical shortage of machine learning expertise across various industries, which could be severely impacting how much benefit companies are able to extract from their AI solutions.
The study by Civo of over 500 software developers, engineers, managers and directors found nearly half (47%) of businesses using ML lack dedicated ML teams.
Furthermore, the lack of training opportunities is hindering onward development, with one-third (36%) of businesses not providing any ML training or education to their workers.
Companies need more ML and AI experts
The research highlights that ML deployment responsibilities are often distributed across various teams and individuals – this lack of streamlining will inevitably make it harder for companies to optimize the deployment of new, time-saving technology solutions.
Only 28% of organizations use dedicated ML engineers using ML tools to run their ML projects, with one in three (33%) tasks typically handled by the wider IT team. Improvised ensemble teams (25%) and non-technical domain experts (7%) are also often tasked with overseeing ML projects.
The study also emphasized the potential of open-source tools and communities in bridging the ML skills gap. More than half (53%) indicated that better access to tools and resources would facilitate ML adoption, with two-thirds (66%) already leveraging open-source ML tools.
Josh Mesout, Chief Innovation Officer for Civo, summarized: “Machine learning deployment is being stymied by a severe shortage of AI talent.”
“By tapping into open source machine learning projects, organizations can leverage shared knowledge, pre-built models, and collaborative development to jumpstart their ML journeys.”
As the world continues to grapple with an ongoing skills shortage, Civo’s study highlights the importance of open-source models in bridging the gap.
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