Dr. Claudia Niederee
Machine Learning (ML) is a subset of Artificial Intelligence (AI) that has gained substantial prominence in diverse economic & social realities, has become the basis for a series of technological developments such as automated translation systems, medical image analysis, and virtual assistants. ML was born from pattern recognition but has evolved to refer to the use of data & learning algorithms to produce models, predict outcomes, and make decisions with minimum human intervention. The fast-paced expansion of ML, especially in data-driven industries (e.g. banking, retail), is rapidly pushing up the demand for skilled ICT workers in the EU. While the demand for ML expert workers is particularly high, the supply is severely lagging behind.
MACHINA is an Erasmus KA2 project, which aims to tackle this ML skill deficit by increasing the relevance of Continuing & Initial VET provision in the sector, to assure that the existing & future ICT workforce will have the ML specific competences & transversal skills required to respond to modern workplace requirements and succeed in a competitive, fast-growing field. The project will also make available transnational educational materials in the form of OERs, to ensure wide adoption and support VET provision in a cost-effective, flexible way.
The EU is now facing a huge ML skills gap, with 769,000 unfilled positions. The demand is particularly growing for ICT workers with the right mix of ML technical (data modeling, software engineering), non-technical (governance, management), and meta-skills (communication, entrepreneurship) to deliver ML solutions that respond to modern world needs for increased efficiency. The goals of the MACHINA project are to tackle this ML skill deficit by designing a joint VET curriculum in ML to empower ICT workers with sought-after technical, non-technical, and meta (soft) skills. Introducing flexible training delivery methods and innovative open-access pedagogical resources to support VET provision and ML skills acquisition. Fostering the recognition and integration of ML skills requirements into sectoral competence frameworks & certification schemes. Improving ML labor market & skills intelligence at the EU level. The project target groups are Educational/Training providers. ICT workers in need of C-VET, I-VET students, sector representatives and social partners, and public educational and accreditation authorities.
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