课程: Foundations of Responsible AI

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Problems in ML that differ from software engineering

Problems in ML that differ from software engineering

课程: Foundations of Responsible AI

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Problems in ML that differ from software engineering

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- Machine learning is unique for various reasons. Despite the fact that many ML models can be created in popular software engineering languages, you can create traditional ML, like logistic regression, in Excel. Technically, you don't need high-powered GPUs and transformers to build the kinds of AI that companies often release. First, you just need a lot of data. It can be argued some methods don't require as much training data, as algorithms like neural networks, but all AI systems are trained on some group of data. How that data is collected, manipulated, and stored is a major concern for customers and users. But these aspects are rarely considered critically when teams build AI systems. Unlike software engineering that allows you to build tools from scratch with few dependencies, the available training data is the main catalyst of ML performance, alongside using the best parameters and optimal settings. Whereas in…

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