But whose job is it to run those steps? And note that most of what I think of as DevOps might have an analogy, but no direct equivalent.
Of course this is all solvable; the thing is that it is new. DevOps assumes that changes are to the code, and has processes and tools for managing code changes.
(I'm biased, as I'm the CTO/co-founder of Efemarai)
Yes, there are usually several changes that need to be tracked - code/model changes (those usually happen early on and the stabalise), input/code changes (e.g. pre-processing the data with either new transformations or _other_ models), and data changes (both changes for training and testing). At Efemarai we are thinking about it as any changes to the above should automatically trigger a test suit of the model/process. And under test we're thinking not just the different forms of unit testing the input/output formats and sizes from the model, but also unit testing on the model performance of the data you've collected + stress testing the model with data the model is expected to see in production.
So in reality, it's indeed nothing new, but the standard DevOps pipeline needs to be extended to work with the ML assumptions.
But whose job is it to run those steps? And note that most of what I think of as DevOps might have an analogy, but no direct equivalent.
Of course this is all solvable; the thing is that it is new. DevOps assumes that changes are to the code, and has processes and tools for managing code changes.
In ML, it is the DATA which changes.