update readme with mi2 finetuning

README.md

@@ -26,6 +26,7 @@ These notebooks show basic patterns that require very little specialized knowled
 * [Zero shot classification with MedImageInsight](./azureml/medimageinsight/zero-shot-classification.ipynb) - learn how to use MedImageInsight to perform zero-shot classification of medical images using its text or image encoding abilities.
 * [Training adapters using MedImageInsight](./azureml/medimageinsight/adapter-training.ipynb) - build on top of zero shot pattern and learn how to train simple task adapters for MedImageInsight to create classification models out of this powerful image encoder. For additional thoughts on when you would use this and the zero shot patterns as well as considerations on fine tuning, [read our blog on Microsoft Techcommunity Hub](https://techcommunity.microsoft.com/blog/healthcareandlifesciencesblog/unlocking-the-magic-of-embedding-models-practical-patterns-for-healthcare-ai/4358000).
 * [Advanced calling patterns](./azureml/medimageinsight/advanced-call-example.ipynb) - no production implementation is complete without understanding how to deal with concurrent calls, batches, efficient image preprocessing, and deep understanding of parallelism. This notebook contains snippets that will help you write more efficient code to build your cloud-based healthcare AI systems.
+* [Fine-tuning MedImageInsight with AzureML Pipelines](./azureml/medimageinsight/finetuning/mi2-finetuning.ipynb) - This detailed notebook demonstrates how to fine-tune the MedImageInsight model using AzureML pipelines. It guides you through setting up prerequisites, data preprocessing, GPU-accelerated training, model deployment, and performance validation, paving the way for state-of-the-art image analysis.
 
 ### Advanced examples and Solution templates