We use MLOps to manually or automatically trigger builds due to Github PRs and changes. The control plane is in DevOps and AzureML Service provides numerous capabilities to track your assets when running Jupyter notebooks local or in the cloud.
This folder demonstrates how to build, train and test notebooks from our Recommendation Project project so you can make your own Recommendation system.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.