Why applied AI requires skills and knowledge beyond data science

Every year, machine learning researchers fascinate us with new discoveries and innovations. There are a dozen artificial intelligence conferences where researchers push the boundaries of science and show how neural networks and deep learning architectures can take on new challenges in areas such as computer vision and natural language processing. But using machine learning in real-world applications and business problems—often referred to as “applied machine learning” or “applied AI”—presents challenges that are absent in academic and scientific research settings. Applied machine learning requires resources, skills, and knowledge that go beyond data science, that can integrate AI algorithms into applications used by thousands and millions of…

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