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How ML Challenges Software Engineering

 3 years ago
source link: https://hackernoon.com/how-ml-challenges-software-engineering-n9w338b
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@jstvssrJoost Visser

Traditional software engineering methods have been designed and optimized to help (teams of) developers to build high-quality software in a controlled and cost-effective manner.

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When building software systems that include Machine Learning (ML) components, those traditional software engineering method are challenged by three distinctive characteristics:

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Inherent uncertainty: ML components insert a new kind of uncertainty into software systems. While software developers and architects are used to design, build, and test their systems to be able to deal with external factors of uncertainty (network latency, unpredictable user behavior, unreliable hardware), they must now deal with internal components that behave in a non-deterministic fashion. ML components map inputs to outputs in a probabilistic fashion. Take for instance an image-recognition component, that categorizes the input as a cat or a dog, with a certain level of probability, rather than having a crisp outcome.

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