Whenever data scientists and AI developers think about the real-world applications of their machine learning models, they don’t use the term “deployment”. Instead, the correct nomenclature would be to operationalize. While sounding confusing for traditional IT application developers and operation managers, the idea is that the approach behind the AI operationalization of a MLOps platform , for example, is different from traditional software deployment. Let’s see below a detailed explanation behind this concept. One of the main differences between an AI project in comparison with another software application development is that it does not follow the standard building, testing, deployment…
Best Practices for Operationalizing AI at Your Enterprise
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