Update README.md

README.md

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 AWS and Hugging Face are working together to simplify and accelerate the adoption of advanced machine learning models. This [collaboration](https://aws.amazon.com/blogs/machine-learning/aws-and-hugging-face-collaborate-to-make-generative-ai-more-accessible-and-cost-efficient/?trk=7902b1b7-22c0-4841-8f9d-50fa299e5e8a&sc_channel=el) offers streamlined training using Hugging Face Deep Learning Containers with SageMaker AI distributed training libraries, simplifying workflows with the SageMaker AI Python SDK for efficient model training. Deployment is made effortless through the Hugging Face Inference toolkit and DLCs, allowing users to deploy trained models on the Hugging Face Hub. Amazon SageMaker AI facilitates the creation of scalable endpoints with built-in monitoring and enterprise-level security. This joint effort empowers teams to move quickly from experimentation to production, leveraging cutting-edge models and scalable infrastructure to drive innovation in machine learning projects.
 
 * Learn about [Hugging Face on AWS](https://aws.amazon.com/ai/hugging-face/?trk=3983b951-6548-4c2c-bd3c-c0429efec685&sc_channel=el)
-* Learn about [Hugging Face in Amazon SageMaker](https://huggingface.co/docs/sagemaker/index)
+* Learn about [Hugging Face on Amazon SageMaker AI](https://huggingface.co/docs/sagemaker/index?trk=0845611a-5b95-4014-9404-c0a21d198643&sc_channel=el)
 * Reference documentation for [using Hugging Face with Amazon SageMaker AI](https://docs.aws.amazon.com/sagemaker/latest/dg/hugging-face.html?trk=c7f1fa13-419b-4f6b-ae36-3a3f67e5bdff&sc_channel=el)
 * [Community Forum](https://discuss.huggingface.co/c/sagemaker/17) on Hugging Face