Merge d56d6a1 into 5a81f9d

Author: brown9804

README.md

@@ -5,7 +5,7 @@ Costa Rica
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-Last updated: 2025-02-21
+Last updated: 2025-04-28
 
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@@ -20,6 +20,20 @@ Last updated: 2025-02-21
 
 </details>
 
+> 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.`
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+| 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  |
+| **Model Deployment**     | Supports real-time and batch deployment via AKS, ACI     | Limited ML deployment; integrates with Azure ML           | Deploys models as APIs or services within projects        |
+| **Compute Options**      | Compute instances, clusters, Kubernetes, attached compute| Uses OneLake and Spark compute for data processing        | Managed compute for model training and inference          |
+| **Notebook Support**     | Jupyter notebooks, VS Code integration                   | Notebooks in Data Science experience (powered by Spark)   | Code-first notebooks and SDK integration                  |
+| **AutoML**               | Built-in AutoML for classification, regression, etc.     | Not available directly                                    | Not primary focus, but supports model selection and tuning|
+| **MLOps & Monitoring**   | Full MLOps support with versioning, CI/CD, monitoring    | Not a core feature                                        | Continuous monitoring and governance for AI apps          |
+| **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     |
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 ## Workspace 
 
 ## Authoring
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 | **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. |
 
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