[Doc CI] Remove invalid link (#3336)

# Description

[[SDK/CLI] Add retry for async calls ·
b04e86a](https://github.com/microsoft/promptflow/actions/runs/9203393562/job/25314843526?pr=3304)

>
D:\a\promptflow\promptflow\docs\sphinx_build.log:597:(integrations/tools/llmlingua-prompt-compression-tool:
line 17) broken
https://learn.microsoft.com/azure/machine-learning/how-to-deploy-models-llama?view=azureml-api-2&tabs=llama-three#deploy-meta-llama-models-with-pay-as-you-go
- Anchor 'deploy-meta-llama-models-with-pay-as-you-go' not found


# All Promptflow Contribution checklist:
- [ ] **The pull request does not introduce [breaking changes].**
- [ ] **CHANGELOG is updated for new features, bug fixes or other
significant changes.**
- [ ] **I have read the [contribution guidelines](../CONTRIBUTING.md).**
- [ ] **Create an issue and link to the pull request to get dedicated
review from promptflow team. Learn more: [suggested
workflow](../CONTRIBUTING.md#suggested-workflow).**

## General Guidelines and Best Practices
- [ ] Title of the pull request is clear and informative.
- [ ] There are a small number of commits, each of which have an
informative message. This means that previously merged commits do not
appear in the history of the PR. For more information on cleaning up the
commits in your PR, [see this
page](https://github.com/Azure/azure-powershell/blob/master/documentation/development-docs/cleaning-up-commits.md).

### Testing Guidelines
- [ ] Pull request includes test coverage for the included changes.

docs/integrations/tools/llmlingua-prompt-compression-tool.md

@@ -14,10 +14,7 @@ PyPI package: [`llmlingua-promptflow`](https://pypi.org/project/llmlingua-prompt
     You may also want to install the [Prompt flow for VS Code extension](https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow).
 
 ## Prerequisite
-Create a MaaS deployment for large language model in Azure model catalog. Take the Llama model as an example, you can learn how to deploy and consume Meta Llama models with model as a service by  [the guidance for Azure AI Studio](https://learn.microsoft.com/azure/ai-studio/how-to/deploy-models-llama?tabs=llama-three#deploy-meta-llama-models-as-a-serverless-api) 
-or
-[the guidance for Azure Machine Learning Studio
-](https://learn.microsoft.com/azure/machine-learning/how-to-deploy-models-llama?view=azureml-api-2&tabs=llama-three#deploy-meta-llama-models-with-pay-as-you-go).
+Create a MaaS deployment for large language model in Azure model catalog. Take the Llama model as an example, you can learn how to deploy and consume Meta Llama models with model as a service by  [the guidance for Azure AI Studio](https://learn.microsoft.com/azure/ai-studio/how-to/deploy-models-llama?tabs=llama-three#deploy-meta-llama-models-as-a-serverless-api).
 
 ## Inputs