workspace added

Author: brown9804

README.md

@@ -13,6 +13,11 @@ Last updated: 2025-04-28
 <details>
 <summary><b>List of References </b> (Click to expand)</summary>
 
+- [Azure Machine Learning pricing](https://azure.microsoft.com/en-us/pricing/details/machine-learning/?msockid=38ec3806873362243e122ce086486339)
+- [Quickstart: Get started with Azure Machine Learning](https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-azure-ml-in-a-day?view=azureml-api-2)
+- [Azure AI Foundry portal or Azure Machine Learning studio: Which experience should I choose?](https://learn.microsoft.com/en-us/ai/ai-studio-experiences-overview?toc=%2Fazure%2Fmachine-learning%2Ftoc.json&bc=%2Fazure%2Fmachine-learning%2Fbreadcrumb%2Ftoc.json&view=azureml-api-2)
+- [Using Azure Machine Learning (AML) for Medical Imaging Vision Model Training and Fine-tuning](https://techcommunity.microsoft.com/blog/machinelearningblog/using-azure-machine-learning-aml-for-medical-imaging-vision-model-training-and-f/4408743)
+
 </details>
 
 <details>
@@ -22,6 +27,8 @@ Last updated: 2025-04-28
 
 > Azure Machine Learning (PaaS) is a cloud-based platform from Microsoft designed to help `data scientists and machine learning engineers build, train, deploy, and manage machine learning models at scale`. It supports the `entire machine learning lifecycle, from data preparation and experimentation to deployment and monitoring.` It provides powerful tools for `both code-first and low-code users`, including Jupyter notebooks, drag-and-drop interfaces, and automated machine learning (AutoML). `Azure ML integrates seamlessly with other Azure services and supports popular frameworks like TensorFlow, PyTorch, and Scikit-learn.`
 
+https://github.com/user-attachments/assets/c199156f-96cf-4ed0-a8b5-c88db3e7a552
+
 | Feature / Platform       | Azure Machine Learning                                  | Microsoft Fabric                                         | Azure AI Foundry                                         |
 |--------------------------|----------------------------------------------------------|-----------------------------------------------------------|-----------------------------------------------------------|
 | **Purpose**              | End-to-end ML lifecycle management and MLOps             | Unified data analytics and business intelligence platform | Unified platform for building and deploying AI solutions  |
@@ -33,9 +40,30 @@ Last updated: 2025-04-28
 | **Target Users**         | Data scientists, ML engineers                            | Data analysts, data scientists, data engineers, developers, business users, executives                           | AI developers, app builders, enterprise teams             |
 | **Integration**          | Azure DevOps, GitHub, MLflow                             | Power BI, Synapse, Azure Data Factory                     | GitHub, VS Code, LangChain, Semantic Kernel, Azure AI     |
 
+> Click here to read more about [Azure AI Foundry portal or Azure Machine Learning studio: Which experience should I choose? - Detailed feature comparison](https://learn.microsoft.com/en-us/ai/ai-studio-experiences-overview?toc=%2Fazure%2Fmachine-learning%2Ftoc.json&bc=%2Fazure%2Fmachine-learning%2Fbreadcrumb%2Ftoc.json&view=azureml-api-2#detailed-feature-comparison)
 
 ## Workspace 
 
+> A taxonomy of the workspace is illustrated in the following diagram, from [What is an Azure Machine Learning workspace?](https://learn.microsoft.com/en-us/azure/machine-learning/concept-workspace?view=azureml-api-2) <br/>
+> **`Compute Instances`**: Preconfigured VMs (CPU/GPU) used for development, experimentation, and running notebooks. <br/>
+> **`Experiments`**: A training run that executes a script with different configurations (e.g., hyperparameters, datasets). It helps track and compare model performance across multiple runs. <br/>
+> **`Model`**: The result of a successful experiment. It represents the trained algorithm and is registered in Azure ML for versioning and reuse. A registered model is essentially the output artifact of an experiment. <br/>
+> **`Endpoint`**: A deployed model exposed as a web service or REST API. Endpoints allow real-time or batch inference. It's important to configure security settings properly, including network access (e.g., VNet integration) and port restrictions. <br/>
+> **`Pipelines`**: Reusable, modular workflows that chain together multiple steps (e.g., data prep, training, evaluation). Useful for automating and orchestrating ML processes. <br/>
+> **`Datasets`**: Versioned data assets used in experiments and pipelines. They ensure consistency and reproducibility across training runs. <br/>
+> **`Registered Models`**: The output of successful experiments. These are trained models saved and versioned for deployment and reuse. <br/>
+> **`Deployment Endpoints`**: Deployed models exposed as REST APIs for real-time or batch inference. Security settings (e.g., VNet, authentication, ports) should be reviewed carefully. <br/>
+
+<div align="center">
+  <img src="https://github.com/user-attachments/assets/f3a987a8-ec59-42de-9e56-29c41c8e55a9" alt="Centered Image" style="border: 2px solid #4CAF50; border-radius: 5px; padding: 5px;"/>
+</div>
+
+Read more about [Endpoints for inference in production](https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints?view=azureml-api-2)
+
+<div align="center">
+  <img src="https://github.com/user-attachments/assets/aa5a0671-e9b8-4ae1-bd69-5098218b63d5" alt="Centered Image" style="border: 2px solid #4CAF50; border-radius: 5px; padding: 5px;"/>
+</div>
+
 ## Authoring
 
 ## Assets
@@ -50,9 +78,7 @@ Last updated: 2025-04-28
 | **Attached Compute** | External compute resources manually connected to Azure ML. | Leverage existing infrastructure. | Using Azure VMs, Databricks, or on-prem compute. | Flexibility, hybrid cloud support, reuse of existing resources. |
 | **Serverless Instances** | Lightweight, on-demand compute (e.g., Azure Container Instances). | Quick testing and low-scale inference. | Temporary model deployment, dev/test environments. | No infrastructure management, fast startup, cost-effective. |
 
-<div align="center">
-  <img src="" alt="Centered Image" style="border: 2px solid #4CAF50; border-radius: 5px; padding: 5px;"/>
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