MLOps isn't just about new technologies and coding practices. Getting better at productionizing your models also likely requires some institutional and/or organisational shifts.
ML practitioners today are embracing data-centric machine learning, because of its substantive effect on MLOps practices. In this article, we take a brief excursion into how data-centric machine learning is fuelling MLOps best practices, and why you should care about this change.
An exploration of some frameworks created by Google and Microsoft that can help think through improvements to how machine learning models get developed and deployed in production.
Software engineering best practices have not been brought into the machine learning space, with the side-effect that there is a great deal of technical debt in these code bases.
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