Merge pull request #281975 from ShawnJackson/azure-ai-studio-conceptual-articles

[AQ] edit pass: Azure AI Studio conceptual articles

Author: ktoliver

articles/ai-studio/concepts/concept-model-distillation.md

@@ -15,28 +15,28 @@ ms.custom: references_regions
 
 # Distillation in Azure AI Studio
 
-In this article
-  - [Distillation](#distillation)
-  - [Next Steps](#next-steps)
+In Azure AI Studio, you can use distillation to efficiently train a student model.
 
-In Azure AI Studio, you can leverage Distillation to efficiently train the student model.
+## What is distillation?
 
-## Distillation
+In machine learning, distillation is a technique for transferring knowledge from a large, complex model (often called the *teacher model*) to a smaller, simpler model (the *student model*). This process helps the smaller model achieve similar performance to the larger one while being more efficient in terms of computation and memory usage.
 
-In machine learning, distillation is a technique used to transfer knowledge from a large, complex model (often called the “teacher model”) to a smaller, simpler model (the “student model”). This process helps the smaller model achieve similar performance to the larger one while being more efficient in terms of computation and memory usage.
+## Distillation steps
 
-The main steps in knowledge distillation involve:
+The main steps in knowledge distillation are:
 
-- **Using the teacher model** to generate predictions for the dataset.
+1. Use the teacher model to generate predictions for the dataset.
 
-- **Training the student model** using these predictions, along with the original dataset, to mimic the teacher model’s behavior.
- 
-You can use the sample notebook available at this [link](https://aka.ms/meta-llama-3.1-distillation) to see how to perform distillation. In this sample notebook, the teacher model used the Meta Llama 3.1 405B Instruct model, and the student model used the Meta Llama 3.1 8B Instruct.
+1. Train the student model by using these predictions, along with the original dataset, to mimic the teacher model's behavior.
 
-We used an advanced prompt during synthetic data generation, which incorporates Chain of thought (COT) reasoning, resulting in higher accuracy data labels in the synthetic data. This further improves the accuracy of the distilled model.
+## Sample notebook
 
-## Next steps
-- [What is Azure AI Studio?](../what-is-ai-studio.md)
-- [Learn more about deploying Meta Llama models](../how-to/deploy-models-llama.md)
+You can use the [sample notebook](https://aka.ms/meta-llama-3.1-distillation) to see how to perform distillation. In this sample notebook, the teacher model uses the Meta Llama 3.1 405B instruction model, and the student model uses the Meta Llama 3.1 8B instruction model.
+
+We used an advanced prompt during synthetic data generation. The advanced prompt incorporates chain-of-thought (CoT) reasoning, which results in higher-accuracy data labels in the synthetic data. This labeling further improves the accuracy of the distilled model.
 
-- [Azure AI FAQ article](../faq.yml)
+## Related content
+
+- [What is Azure AI Studio?](../what-is-ai-studio.md)
+- [Deploy Meta Llama 3.1 models with Azure AI Studio](../how-to/deploy-models-llama.md)
+- [Azure AI Studio FAQ](../faq.yml)

articles/ai-studio/concepts/concept-synthetic-data.md

@@ -1,7 +1,7 @@
 ---
 title: Synthetic data generation in AI Studio
 titleSuffix: Azure AI Studio
-description: Learn how to generate Synthetic dataset in Azure AI Studio.
+description: Learn how to generate a synthetic dataset in Azure AI Studio.
 manager: scottpolly
 ms.service: azure-ai-studio
 ms.topic: how-to
@@ -15,23 +15,26 @@ ms.custom: references_regions
 
 # Synthetic data generation in Azure AI Studio
 
-In Azure AI Studio, you can use synthetic data generation to efficiently produce predictions for your datasets. In this article, you're introduced to the concept of synthetic data generation and how it can be used in machine learning.
+In Azure AI Studio, you can use synthetic data generation to efficiently produce predictions for your datasets. This article introduces you to the concept of synthetic data generation and how you can use it in machine learning.
 
+## What is synthetic data generation?
 
-## Synthetic data generation
+Synthetic data generation involves creating artificial data that mimics the statistical properties of real-world data. This data is generated through algorithms and machine learning techniques. You can use the data in various ways, such as computer simulations or modeling real-world events.
 
-Synthetic data generation involves creating artificial data that mimics the statistical properties of real-world data. This data is generated using algorithms and machine learning techniques, and it can be used in various ways, such as computer simulations or by modeling real-world events.
+## Benefits
 
-In machine learning, synthetic data is valuable for several reasons:
+In machine learning, synthetic data is valuable for:
 
-**Data Augmentation:** It helps in expanding the size of training datasets, which is crucial for training robust machine learning models. This is especially useful when real-world data is scarce or expensive to obtain.
+- **Data augmentation**: It helps in expanding the size of training datasets, which is crucial for training robust machine learning models. This expansion technique is especially useful when real-world data is scarce or expensive to obtain.
 
-**Testing and Validation:** It allows for extensive testing and validation of machine learning models under various scenarios without the need for real-world data.
+- **Testing and validation**: It allows for extensive testing and validation of machine learning models under various scenarios without the need for real-world data.
 
-You can use the sample notebook available at this [link](https://aka.ms/meta-llama-3.1-datagen) to see how to generate Synthetic data.
+## Sample notebook
 
-## Next steps
-- [What is Azure AI Studio?](../what-is-ai-studio.md)
-- [Learn more about deploying Meta Llama models](../how-to/deploy-models-llama.md)
+To see how to generate synthetic data, you can use the [sample notebook](https://aka.ms/meta-llama-3.1-datagen).
+
+## Related content
 
-- [Azure AI FAQ article](../faq.yml)
+- [What is Azure AI Studio?](../what-is-ai-studio.md)
+- [Deploy Meta Llama 3.1 models with Azure AI Studio](../how-to/deploy-models-llama.md)
+- [Azure AI Studio FAQ](../faq.yml)