arXiv:2606.08212v1 Announce Type: new Abstract: Solving machine learning problems is complex and typically reserved for experts. Over the past two decades, systems have emerged to support non-experts. Based on our review, we identify three categories: (1) fully automated AutoML systems, (2) expert cheat sheets for algorithm selection, and (3) decision-support systems using selection criteria (accuracy, transparency, data requirements). We propose a new platform combining categories 2 and 3 to deliver semi-automated, intelligent solution recommendations for non-experts. Unlike existing approach
The growing complexity and pervasive application of machine learning necessitate tools that broaden accessibility beyond expert practitioners.
Democratizing machine learning capabilities could accelerate innovation and application across various industries, reducing the bottleneck of specialized expertise.
The development of semi-automated, intelligent platforms simplifies machine learning problem-solving, making it accessible to a wider pool of users.
- · SMEs and startups
- · Non-expert data scientists
- · AI platform providers
- · ML consultancy firms focused solely on basic problem-solving
- · Highly specialized ML tools with steep learning curves
Increased adoption of machine learning solutions in domains previously limited by a lack of expert talent.
A shift in demand for AI professionals from basic model building to more complex architecture design and integration.
The emergence of entirely new application areas and business models enabled by broader ML accessibility.
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Read at arXiv cs.LG