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The Pragmatic Programmer for Machine Learning (2023)
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Likewise, we do not believe radiologists or lorry drivers will be replaced by artificial intelligence and out of a job for the foreseeable future, and we are not alone in realising the limits of machine learning (The Economist 2020). In industry, poor engineering leads to lower practical and computational performance (Kang et al. 2021), to a quick accumulation of technical debt (Sculley et al. 2015) and sometimes to catastrophic failures with costs in the millions (.Seven 2014; The Register 2020; VPNOverview 2022; Sherman 2022). However, these books are written with business software in mind, and we find that they do not capture or touch only tangentially on key practices that go a long way towards successfully implementing and deploying machine learning models.
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