Not only do we have to manage the software code artifacts but also the data sets, the machine learning models, and the parameters and hyperparameters used by such models. All these artifacts have to be managed, versioned and promoted through different stages until they’re deployed to production.
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Sato,Wider,Windheuser_2019_Continuous-delivery_thoughtworksicient collaboration and alignment. However, this integration also brings new challenges when compared to traditional software development. These include: A higher number of changing artifacts. <span>Not only do we have to manage the software code artifacts but also the data sets, the machine learning models, and the parameters and hyperparameters used by such models. All these artifacts have to be managed, versioned and promoted through different stages until they’re deployed to production. It’s harder to achieve versioning, quality control, reliability, repeatability and audibility in that process. Size and portability: Training data and machine learning models usually co Summary
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