Machine learning (ML) began moving toward solving practical problems in the 1990s, but in the last decade the explosion in data volume and processor power brought machine learning calculations within the needs and ability of any business.
While early data scientists in the 1950s saw ML as a part of Artificial Intelligence (AI) and focused on machines that could learn from data, by the 1970s ML was seen as its own field. Focused on pattern recognition and similar concepts, ML became widely applied to practical problem-solving with data. As data-driven decisioning increased across business domains, the data velocity, variety, and volume (the 3Vs) increased exponentially. Infrastructure of the last decade (high-speed internet, cheap storage, and processing power) was applied to harness the data, using ML to interpret, describe, and even predict results.
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