> For the complete documentation index, see [llms.txt](https://docs.probe.splx.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.probe.splx.ai/ai-red-teaming/probe/target/index/azure-ml.md).

# Azure ML

## Selecting the Connection Type

Once you have [selected your connection type](/ai-red-teaming/probe/target/add-target/integration-setup.md#selecting-a-connection-type), a configuration tab will appear on the next step, prompting you to input the required connection details.

## Integration Setup

<figure><img src="/files/d5B6iAtnbM9kLTKKHw8A" alt=""><figcaption><p>Figure 1: Azure ML Integration Example</p></figcaption></figure>

* **System Prompt** - Your application’s system prompt. It sets the initial instructions or context for the AI model, defines the behavior, tone, and specific guidelines the AI should follow while interacting. For best practices, refer to the [OpenAI documentation on prompt engineering](https://platform.openai.com/docs/guides/prompt-engineering).
* **API Key**&#x20;
  * In Azure Machine Learning Studio, select the workspace on the [Workspaces page](https://ml.azure.com/workspaces).&#x20;
  * From the navigation bar, open the Endpoints  page and select the Serverless endpoints tab.
  * Open your endpoint from the list.&#x20;
  * Copy the Key and insert it into the Probe integration input field.
* **URL** - On the same serverless endpoint where you got the API key, the required URL can be found in the Target URI field. Copy this URL and insert it into the Probe integration input field.

For more information, you can explore the official  [Azure Machine Learning](https://learn.microsoft.com/en-us/azure/machine-learning/) documentation.


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