docs: fix broken links

[mhamilton723]

Related Issues/PRs

#xxx

What changes are proposed in this pull request?

Briefly describe the changes included in this Pull Request.

How is this patch tested?

• I have written tests (not required for typo or doc fix) and confirmed the proposed feature/bug-fix/change works.

Does this PR change any dependencies?

• No. You can skip this section.
• Yes. Make sure the dependencies are resolved correctly, and list changes here.

Does this PR add a new feature? If so, have you added samples on website?

• No. You can skip this section.
• Yes. Make sure you have added samples following below steps.

1. Find the corresponding markdown file for your new feature in website/docs/documentation folder.
   Make sure you choose the correct class estimators/transformers and namespace.
2. Follow the pattern in markdown file and add another section for your new API, including pyspark, scala (and .NET potentially) samples.
3. Make sure the DocTable points to correct API link.
4. Navigate to website folder, and run yarn run start to make sure the website renders correctly.
5. Don't forget to add <!--pytest-codeblocks:cont--> before each python code blocks to enable auto-tests for python samples.
6. Make sure the WebsiteSamplesTests job pass in the pipeline.

[github-actions]

Hey @mhamilton723 👋!
Thank you so much for contributing to our repository 🙌.
Someone from SynapseML Team will be reviewing this pull request soon.

We use semantic commit messages to streamline the release process.
Before your pull request can be merged, you should make sure your first commit and PR title start with a semantic prefix.
This helps us to create release messages and credit you for your hard work!

Examples of commit messages with semantic prefixes:

• fix: Fix LightGBM crashes with empty partitions
• feat: Make HTTP on Spark back-offs configurable
• docs: Update Spark Serving usage
• build: Add codecov support
• perf: improve LightGBM memory usage
• refactor: make python code generation rely on classes
• style: Remove nulls from CNTKModel
• test: Add test coverage for CNTKModel

To test your commit locally, please follow our guild on building from source.
Check out the developer guide for additional guidance on testing your change.

[github-actions]

Summary by GPT-4

In this notebook, we explore advanced usage of Cognitive Services with Synapse ML, including asynchronous processing, batching, and multi-key support. We demonstrate how to analyze images and text using the AnalyzeImage and TextSentiment transformers.

The notebook is organized into the following steps:

1. Imports and Keys: Import necessary libraries and set up the service key.
2. Basic Usage: Analyze images using the AnalyzeImage transformer.
3. Asynchronous Usage: Improve throughput by allowing workers to process multiple requests concurrently.
4. Batching: Process multiple text inputs in a single request using the TextSentiment transformer.
5. Multi-Key: Use multiple subscription keys to distribute requests across different keys.

By the end of this notebook, you will have a better understanding of how to use Cognitive Services with Synapse ML for advanced scenarios, improving performance and efficiency in your machine learning workflows.

Suggestions

No suggestions are needed as the PR has no clear issues.