Deployments can be generated using both the Python API or MLflow CLI. In both cases, a JSON configuration file can be indicated with the details of the deployment you want to achieve. If not indicated, then a default deployment is done using Azure Container Instances (ACI) and a minimal configuration. The full specification of this configuration file can be checked at Deployment configuration schema. Also, you will also need the Azure ML MLflow Tracking URI of your particular Azure ML Workspace where you want to deploy your model. You can obtain this URI in several ways:
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