The introduction of the machine learning and AI into the industry will provide to the production chain enormous benefits at many levels, making real new kind of manufacturing processes and delivering to the market better products and services, AI will permit to optimize the costs and the resources to achieve the best level of quality and productivity.
Machine learning is a substantially a computational process. To that end, it is inextricably tied to computational power and computing architectures. The computational power and the computing architecture shape the speed of training and inference in machine learning and therefore influence the rate of progress in the technology. But, these relationships are more nuanced than that: hardware shapes the methods used by researchers and engineers in the design and development of machine learning models.
This paper aims to dig more deeply into the relationship between computational power and the development of machine learning chowing how the right computing architecture and the capability to fit the problem needs permits to achieve better results in shorter time and opens many new opportunities in the adoption of the AI into the industry.
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