Forum Discussion
Best Way to do CICD for data Science workloads
Hi Srisakthi , for ML Models, you have 2 options. You can use the built-in Fabric deployment pipelines or you can leverage a Git integration to use a service such as Azure DevOps. Both are fully supported. Here is some documentation below:
Hi Srisakthi ,
Which data science workloads are you leveraging? For CI/CD in Fabric, there are a few methodologies you can use. The one I have used is connecting Azure DevOps to a development workspace and leveraging the built-in Fabric Deployment Pipelines as a release pipeline to subsequent environments (test/prod). Here are some additional documentation on methodologies here: https://learn.microsoft.com/en-us/fabric/cicd/manage-deployment
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Thanks,
Samson
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Hi SamsonTruong ,
The link which you have shared is a generic Fabric CICD. I'm looking for is Data Science Workloads.
To give you high level information of data science workload is all about creating deploying ML models, data agents etc.
Regards,
Srisakthi
Hi Srisakthi ,
depending on which data science workloads you are using, the Fabric CICD is still a viable option. Using the built in Fabric deployment pipelines, you have support for notebooks, ML experiments, and ML models. Can you please provide additional information into which specific data science workloads you are working with, as the recommended CICD infrastructure may change based on your use case.
If this helped, please mark it as the solution so others can benefit too. And if you found it useful, kudos are always appreciated.
Thanks,Samson
Connect with me on LinkedIn
Check out my Blog
Going to the European Microsoft Fabric Community Conference? Check out my Session
Hi SamsonTruong ,
Thanks for your response. It helps me. My scenario is i have multiple datascience workpsaces and each has ML models. i need to promote all these ML models from different workspace to one workspace in my test environment. What is the best approach i can chose?
Regards,
Sri
Hi Srisakthi , for ML Models, you have 2 options. You can use the built-in Fabric deployment pipelines or you can leverage a Git integration to use a service such as Azure DevOps. Both are fully supported. Here is some documentation below: