include finetuning modules in changelog

Author: mmiakashs

CHANGELOG.md

@@ -9,6 +9,29 @@ SPDX-License-Identifier: MIT-0
 
 ### Added
 
+- **Amazon Nova Model Fine-tuning Support**
+  - Added comprehensive `ModelFinetuningService` class for managing Nova model fine-tuning workflows
+  - Support for fine-tuning Amazon Nova models (Nova Lite, Nova Pro) using Amazon Bedrock
+  - Complete end-to-end workflow including dataset preparation, job creation, provisioned throughput management, and inference
+  - CLI tools for fine-tuning workflow:
+    - `prepare_nova_finetuning_data.py` - Dataset preparation from RVL-CDIP or custom datasets
+    - `create_finetuning_job.py` - Fine-tuning job creation with automatic IAM role setup
+    - `create_provisioned_throughput.py` - Provisioned throughput management for fine-tuned models
+    - `inference_example.py` - Model inference and evaluation with comparison capabilities
+  - CloudFormation integration with new parameters:
+    - `CustomClassificationModelARN` - Support for custom fine-tuned classification models in Pattern-2
+    - `CustomExtractionModelARN` - Support for custom fine-tuned extraction models in Pattern-2
+  - Automatic integration of fine-tuned models in classification and extraction model selection dropdowns
+  - Comprehensive documentation in `docs/nova-finetuning.md` with step-by-step instructions
+  - Example notebooks:
+    - `finetuning_dataset_prep.ipynb` - Interactive dataset preparation
+    - `finetuning_model_service_demo.ipynb` - Service usage demonstration
+    - `finetuning_model_document_classification_evaluation.ipynb` - Model evaluation
+  - Built-in support for Bedrock fine-tuning format with multi-modal capabilities
+  - Data splitting and validation set creation
+  - Cost optimization features including provisioned throughput deletion
+  - Performance metrics and accuracy evaluation tools
+
 - **Assessment Feature for Extraction Confidence Evaluation (EXPERIMENTAL)**
   - Added new assessment service that evaluates extraction confidence using LLMs to analyze extraction results against source documents
   - Multi-modal assessment capability combining text analysis with document images for comprehensive confidence scoring