There is a command group 'az_ml_data'. This group of commands could 'manage azure ml data assets'. 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_data_create'.The syntax is: az ml data create [--datastore]
                  [--description]
                  [--file]
                  [--name]
                  [--no-wait]
                  [--path]
                  [--registry-name]
                  [--resource-group]
                  [--set]
                  [--skip-validation]
                  [--type {mltable, uri_file, uri_folder}]
                  [--version]
                  [--workspace-name]. The 1th example of the child command would be to use it to 'create a data asset from a yaml specification file in a workspace', the syntax is: az ml data create --file data.yml --resource-group my-resource-group --workspace-name my-workspace. The 2th example of the child command would be to use it to 'create a data asset from a yaml specification file in a registry', the syntax is: az ml data create --file data.yml --registry-name my-registry-name. The 3th example of the child command would be to use it to 'create a data asset without using a yaml specification file in a workspace', the syntax is: az ml data create --name my-data --version 1 --path ./my-data.csv --resource-group my-resource-group --workspace-name my-workspace. The 4th example of the child command would be to use it to 'create a data asset without using a yaml specification file in a registry', the syntax is: az ml data create --name my-data --version 1 --path ./my-data.csv --registry-name my-registry-name. The parameter '--datastore' is optional. the datastore to upload the local artifact to.. The parameter '--description -d' is optional. description of the data asset.. The parameter '--file -f' is optional. local path to the yaml file containing the azure ml data specification. the yaml reference docs for data can be found at: https://aka.ms/ml-cli-v2-data-yaml-reference.. The parameter '--name -n' is optional. name of the data asset. required if --registry-name is provided.. The parameter '--no-wait' is optional. do not wait for the long-running-operation to finish. default is false.. The parameter '--path -p' is optional. path to the data asset, can be local or remote.. The parameter '--registry-name' is optional. if provided, the command will target the registry instead of a workspace. hence resource group and workspace won't be required. must be provided if --workspace-name and --resource-group are not provided.. The parameter '--resource-group -g' is optional. name of resource group. you can configure the default group using `az configure --defaults group=<name>`.. 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-validation' is optional. skip validation of mltable metadata when type is mltable.. The parameter '--type -t' is optional. type of the data asset.. The parameter '--version -v' is optional. version of the data asset. required if --registry-name is provided.. The parameter '--workspace-name -w' is optional. name of the azure ml workspace. you can configure the default workspace using `az configure --defaults workspace=<name>`.. 