287 latest curated summaries of LLMOps use cases in industry, from tech to healthcare to finance and more. This blog also highlights some of the trends observed across the case studies.
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.
The MLOps movement and associated new tooling is starting to help tackle the very real technical debt problems associated with machine learning in production.
Using config files to specify infrastructure for training isn't widely practiced in the machine learning community, but it helps a lot with reproducibility.
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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