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The Way to Applied Machine Learning

Machine learning researchers astonish us with new discoveries and inventions every year. A dozen artificial intelligence conferences exist where researchers push the limits of science and demonstrate how neural networks and deep learning architectures can tackle new problems in areas including computer vision and natural language processing.

However, applying machine learning to real-world applications and business problems, also known as "applied machine learning" or "applied AI," poses obstacles that aren't present in academic and science study. Applied machine learning necessitates tools, expertise, and knowledge that extend beyond data science, allowing AI algorithms to be integrated into applications that are used by thousands or millions of people on a daily basis.

In their latest book Real World AI: A Practical Guide for Responsible Machine Learning, Alyssa Simpson Rochwerger and Wilson Pang, two accomplished practitioners of applied machine learning, address these challenges. Rochwerger, a former director of product at IBM Watson, and Pang, the CTO of Appen, use their personal experiences and expertise to provide several examples of how businesses have successfully or unsuccessfully integrated machine learning into their products and business models.

Real World AI discusses how product leaders can avoid making the mistakes of others by understanding the common problems and drawbacks of machine learning strategies. Here are four of the major issues posed by Rochwerger and Pang in their novel.

Defining the Problem