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Commonly models need to be retrained and redeployed to an existing deployment without endpoint URL or credentials changing - API Docs
It is already supported in latest version of client library:
there is a method
`client.deployments.update()` ```def update(self, deployment_uid, name=None, description=None, asynchronous=False, meta_props=None): """ Update model used in deployment to the latest version. The scoring_url remains. Name and description change will not work for online deployment. For virtual deployments the file will be updated under the same download_url. :param deployment_uid: Deployment UID :type deployment_uid: str :param name: new name for deployment :type name: str :param description: new description for deployment :type description: str :param meta_props: dictionary with parameters used for virtual deployment (Core ML format) :type meta_props: dict :returns: updated metadata of deployment :rtype: dict A way you might use me is: >>> deployment_details = client.deployments.update(deployment_uid)```
there is also corresponding method for model update …
```def update_model(self, model_uid, content_path=None, meta_props=None): """ Update content of model with new one. :param model_uid: Model UID :type model_uid: str :param content_path: path to tar.gz with new content of model :type content_path: str :returns: updated metadata of model :rtype: dict A way you might use me is: >>> model_details = client.repository.update_model_content(model_uid, content_path) """```
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