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Training data and machine learning models usually come in volumes that are orders of magnitude higher than the size of the software code. As such they require different tools that are able to handle them efficiently. These tools impede the use of a single unified format to share those artifacts along the path to production, which can lead to a “throw over the wall” attitude between different teams.
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Sato,Wider,Windheuser_2019_Continuous-delivery_thoughtworks
rough 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: <span>Training data and machine learning models usually come in volumes that are orders of magnitude higher than the size of the software code. As such they require different tools that are able to handle them efficiently. These tools impede the use of a single unified format to share those artifacts along the path to production, which can lead to a “throw over the wall” attitude between different teams. Different skills and working processes in the workforce: To develop machine learning applications, experts with complementary skills are necessary, and they sometimes have contradicting


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