The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Machine learning is becoming an important tool in many industries and fields of science. But ML research and product development present several challenges that, if not addressed, can steer your project in the wrong direction. In a paper recently published on the arXiv preprint server, Michael Lones, Associate Professor in the School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, provides a list of dos and don'ts for machine learning research. The paper, which Lones describes as "lessons that were learnt whilst doing ML research in academia, and whilst supervising students doing ML research," covers the challenges of different stages of the machine learning research lifecycle. Although aimed at academic researchers, the paper's guidelines are also useful for developers who are creating machine learning models for real-world applications.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Machine learning is becoming an important tool in many industries and fields of science. But ML research and product development present several challenges that, if not addressed, can steer your project in the wrong direction. In a paper recently published on the arXiv preprint server, Michael Lones, Associate Professor in the School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, provides a list of dos and don'ts for machine learning research. The paper, which Lones describes as "lessons that were learnt whilst doing ML research in academia, and whilst supervising students doing ML research," covers the challenges of different stages of the machine learning research lifecycle. Although aimed at academic researchers, the paper's guidelines are also useful for developers who are creating machine learning models for real-world applications.