CI/CD¶
This section includes some ideas for model developmnet and deployment within Azure services.
- MLFlow for experiment tracking and endpoint deployment
- https://mlflow.org/docs/1.25.1/python_api/mlflow.azureml.html
- https://mlflow.org/docs/latest/deployment/index.html
- https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models-online-endpoints?view=azureml-api-2&tabs=mlflow
- DVC for dataset versioning
- python api: https://dvc.org/doc/api-reference
- Dataset storage in Azure Blob Storage
- Dataset and predictions(dev) visualization from Azure Blob Storage
- Data handling in PROD
- databricks: https://learn.microsoft.com/en-us/azure/databricks/getting-started/data-pipeline-get-started
- create and share dashboards within organization: https://learn.microsoft.com/en-us/azure/databricks/sql/get-started/sample-dashboards