狼群
发表于 2025-3-23 12:00:41
978-3-031-00557-2Springer Nature Switzerland AG 2021
裂口
发表于 2025-3-23 14:31:57
Why AI/Data Science Projects Fail978-3-031-01685-1Series ISSN 2766-8975 Series E-ISSN 2766-8967
TSH582
发表于 2025-3-23 20:12:12
Synthesis Lectures on Computation and Analyticshttp://image.papertrans.cn/w/image/1028106.jpg
Enteropathic
发表于 2025-3-23 23:06:32
Introduction and Background,jects failed. Looking into it, I learned that in this case, failure was defined as “not being deployed.” So, starting a project that didn’t make it to production was the definition of the project failing. I was surprised by the number. 85% is a large percentage.
吝啬性
发表于 2025-3-24 04:00:04
,,get resources and funding. It helps to answer management’s question of “what do I get?” Knowing the expected deliverables for a project helps in getting support. It also helps with defining when you are done with a project.
breadth
发表于 2025-3-24 08:59:12
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外露
发表于 2025-3-24 11:08:04
Project Phases and Common Project Pitfalls,Let’s take a more in-depth look into the reasons for projects not to get to production. While there are many reasons, and clearly this must be true because 87% of projects don’t make it, I’ll cover 5 reasons that are systematic in nature. These reasons are:
思想
发表于 2025-3-24 16:48:01
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准则
发表于 2025-3-24 21:34:04
Model-Building Phase,Two of the project pitfalls relate directly to the model building phase of a data science project: couldn’t explain the model, and the model was too complex. The tools to address these pitfalls are to keep things simple and leverage explainability.
Libido
发表于 2025-3-25 00:02:25
Summary of the Five Methods to Avoid Common Pitfalls,The current statistic is that 87% of AI/big data projects fail. In this context, failing means that the project never reaches deployment. By applying 5 methods to avoid common pitfalls, you can give your project a better opportunity to beat the odds and be one of the 13% that make it to production. The five pitfalls are: