There is a command group 'az_ml_online deployment'. This group of commands could 'manage azure ml online deployments'. The group of commands has global parameter '--debug' which means 'Increase logging verbosity to show all debug logs.'. The group of commands has global parameter '--help -h' which means 'Show this help message and exit.'. The group of commands has global parameter '--only-show-errors' which means 'Only show errors, suppressing warnings.'. The group of commands has global parameter '--output -o' which means 'Output format.'. The group of commands has global parameter '--query' which means 'JMESPath query string. See <a href="http://jmespath.org/">http://jmespath.org/</a> for more information and examples.'. The group of commands has global parameter '--subscription' which means 'Name or ID of subscription. You can configure the default subscription using `az account set -s NAME_OR_ID`.'. The group of commands has global parameter '--verbose' which means 'Increase logging verbosity. Use --debug for full debug logs.'. This group contains a child command 'az_ml_online deployment_create'.The syntax is: az ml online-deployment create --file
                               --resource-group
                               --workspace-name
                               [--all-traffic]
                               [--endpoint-name]
                               [--local {false, true}]
                               [--local-enable-gpu {false, true}]
                               [--name]
                               [--no-wait]
                               [--package-model]
                               [--set]
                               [--skip-script-validation]
                               [--vscode-debug {false, true}]
                               [--web]. The 1th example of the child command would be to use it to 'create a deployment from a yaml specification file', the syntax is: az ml online-deployment create --file deployment.yaml --resource-group my-resource-group --workspace-name my-workspace. The parameter '--file -f' is required. It is used to local path to the yaml file containing the azure ml online-deployment specification. the yaml reference docs for online-deployment can be found at: https://aka.ms/ml-cli-v2-deployment-managed-online-yaml-reference, https://aka.ms/ml-cli-v2-deployment-kubernetes-online-yaml-reference.. The parameter '--resource-group -g' is required. It is used to name of resource group. you can configure the default group using `az configure --defaults group=<name>`.. The parameter '--workspace-name -w' is required. It is used to name of the azure ml workspace. you can configure the default workspace using `az configure --defaults workspace=<name>`.. The parameter '--all-traffic' is optional. sets endpoint traffic 100% to this deployment after successful creation, does not work with --no-wait.. The parameter '--endpoint-name -e' is optional. name of the online endpoint.. The parameter '--local' is optional. create deployment locally using docker. only one deployment per endpoint is allowed. note: if specified endpoint doesn't exist, it will be created.. The parameter '--local-enable-gpu' is optional. enable gpu for local deployment.. The parameter '--name -n' is optional. name of the deployment.. The parameter '--no-wait' is optional. do not wait for the long-running operation to finish.. The parameter '--package-model' is optional. [this is in preview] create packaged environment from the deployment yaml and use the packaged environment for the deployment.. The parameter '--set' is optional. update an object by specifying a property path and value to set. example: --set property1.property2=<value>.. The parameter '--skip-script-validation' is optional. allows user to bypass deployment scoring script validation.. The parameter '--vscode-debug' is optional. create local endpoint and attach vscode debugger. only works with --local flag.. The parameter '--web' is optional. show the deployment's details in azure ml studio in a web browser.. 