title | titleSuffix | description | services | ms.service | ms.subservice | ms.custom | ms.topic | author | ms.author | ms.date | ms.reviewer | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
CLI (v2) serverless connection YAML schema |
Azure Machine Learning |
Reference documentation for the CLI (v2) serverless connections YAML schema. |
machine-learning |
machine-learning |
core |
|
reference |
Blackmist |
larryfr |
04/15/2024 |
ambadal |
[!INCLUDE cli v2]
[!INCLUDE schema note]
Key | Type | Description | Allowed values | Default value |
---|---|---|---|---|
$schema |
string | The YAML schema. If you use the Azure Machine Learning Visual Studio Code extension to author the YAML file, include $schema at the top of your file to invoke schema and resource completions. |
||
name |
string | Required. The connection name. | ||
description |
string | The connection description. | ||
tags |
object | The connection tag dictionary. | ||
type |
string | Required. The connection type. | serverless |
sereverless |
is_shared |
boolean | true if the connection is shared across other projects in the hub; otherwise, false . |
true |
|
endpoint |
string | Required. The serverless endpoint for this connection. | ||
api_key |
string | Required. The API key used to authenticate the connection. |
The schema described in this article is used to create a serverless connection.
While the az ml connection
commands can be used to manage both Azure Machine Learning and Azure AI Studio connections, the Azure AI Speech Services connection is specific to Azure AI Studio.
Visit this GitHub resource for examples. Several are shown here. These examples would be in the form of YAML files and used from the CLI. For example, az ml connection create -f <file-name>.yaml
.
#ServerlessConnection.yml
name: my_maas_apk
type: serverless_model
endpoint: https://serverless.endpoint.net/
api_key: XXXXXXXXXXXXXXX