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In our backend development process, we have two environments: testing and production. We develop our code, and then we push the code into the testing repository. Then on the release date, we push everything into production.

Now that we are going to use ML studio, I'm struggling with setting up testing and production environments for my ML studio experiments.

I created two identical experiments with independent APIs; one experiment for testing and the other experiment is used by the production. When it comes to moving the trained experiment from testing to production, I make all the changes I made in the testing environment to the production environment, which is a very time demanding process.

Do you know any better solution so we can deploy and test our changes and then deploy the latest changes to the production? How people use ML studio in their CD/CI process?

The attached image shows the design that I have now. I'd appreciate if you can help me in improving this process. Maybe ML studio has some features to manage this scenario that I don't know.

Geshode
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S. Think
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1 Answers1

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In MLStudio, when you doing publishing an experiment as a API, the current API get replaced with a prevailing one. One of the practices you can do is like this. Maintain a test experiment. Maintain an identical copy of that and push it for the production. When you done with the changes in the test experiment keep it as that (Then you can change it later) and make a copy of that(Using Save as) - publish it as the production service.

There are some drawbacks in this too. You have to update the API endpoints on the production code once you going to release and you may have to document the versions going for the production manually. The only advantage is the time of updating two experiments goes off.

Haritha Thilakarathne
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