Machine learning model retraining on Azure Devops

Question

I am building a pipeline Azure Devops. The model is in a .py file which connect to a database to extract training data and output a pickle file with the model.
I would like to trigger this script every time there is a database update.
I use a .yaml file pipeline where I build and run a docker container with the model inside.
I checked the possible way to trigger the .yaml pipeline and could not find a way to do that.
What is the best way to achieve this? Should it be on it's own .yaml pipeline?
I just started to use Azure devops and I am not sure what is the best practice in this case.

Thank you in advance.

Answer 1

Hi,

You can try the Microsoft learn modules, create-a-build-pipeline also try the lab-services Lab Services and this way you can understand the process and test the required deployment process.

Some of the best practices - phase-rollout-with-rings

Hope this helps.

==
Please "Accept the answer" if the information helped you. This will help us and others in the community as well.

Answer 2

@JimmySalian-2011 Thank you for the answer. I am sorry I am still a bit lost with this. Could you write your guidance here about this. I would like to trigger the script .py which is in a Docker container everytime there is a db update. Using Azure devops, what, where and how should be the trigger please?

Answer 3

Is UDF the good solution?
https://stackoverflow.com/questions/23382499/run-python-script-on-database-event