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The intention is to get the Data Scientists out of the habit of working in isolation on their laptops for months and focus them on being regular and iterative collaborators to an MLOps team in a distributed production-capable environment.
In practice, most of the DS team seem keen to maintain the service component of their ML solution and as a result are learning very rapidly about the practical challenges of scaling distributed solutions effectively.
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On 7 Jan 2020, at 01:12, Michael Neale <mneale@...> wrote:
That is pretty cool Terry.
With the training repo - is the idea that datascience type persons work in that, play with their data/model/notebooks, and when ready commit or issue a pull request to the *training* repo? do they then look at the PR in the service repo to test the real thing out? what is the workflow between data science experts and the engineers that work on the service/app that you see out there?
On Fri, Dec 20, 2019 at 5:41 AM Terry Cox <terry@...
As discussed in the SIG meeting, here is a link to a quick demo
video of MLOps within Jenkins-X. Things are developing fairly
quickly and we plan to have a broad range of template projects in
our public repo in the new year.
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