Azure Machine Learning - Public Preview for Build

[Expand AzureML’s Responsible AI dashboard to support text/image classification scenarios](https://aka.ms/RAI%5Ftext%5Fimage): You can now create and generate Responsible AI dashboards for text and image models from CLI and SDK. [Link Azure Machine Learning workspace to Purview catalog](https://microsoft.sharepoint.com/teams/MachineLearningPlatformmarketing/Shared%20Documents/General/Readiness/Field%20Azure%20updates%20slides%20for%20gearUp/Build%202023/ACOM%20Pages/PuPr%20Tier%202%20Build%202023%20ACOM.docx): You can now automatically push relevant metadata of assets, such as models, jobs, and datasets to the Purview catalog. [Hugging Face foundation models in AzureML](https://aka.ms/AzureML%5FFoundationModels): You can now build and operationalize open source SOTA models at scale. [AzureML PromptFlow](https://aka.ms/prompt%5Fflow): You can now create AI workflows that connect to various language models and data sources. [Managed Feature Store](https://aka.ms/ManagedFS): You can now experiment and ship models faster, increase reliability of your models and reduce your operational costs. [Perform continuous model monitoring](https://aka.ms/azureml-momo/doc): You can now proactively find and resolve issues faster, and continuously improve models for enhanced quality and compliance. [Manage Network Isolation](https://aka.ms/MNI): You can now streamline your network isolation experience, speed up your workspace setup, and free yourself from the hassles of virtual network management. [Track, compare, and visualize your training jobs with our improved experiment tracking tools](https://aka.ms/Improved%5FTracking): You can now quickly investigate, compare, and summarize your experimentation results with various chart types and markdown functionality that you can customize to your desired preference in a new dashboard view. [Model Training with Serverless Compute](https://aka.ms/MTSC): You can now focus on your job spec without having to learn about compute and how to set it up. [Import data from external sources for training in AzureML platform](https://aka.ms/ImportExternalData): You can now import data from various external sources right from AzureML without dependency on other tools or teams. [Connect compute instances to Visual Studio Code for the Web](https://aka.ms/VSCodeWeb): You can now continue your work directly in the browser connected to an AzureML compute instance without needing to download or install an application. [Deploy pipelines and components under Batch Endpoints](https://aka.ms/Batch%5FEndpoints): You can now deploy complex compute graphs under batch endpoints to perform batch inference. * Azure Machine Learning * Features * Microsoft Build * [ Azure Machine Learning](https://azure.microsoft.com/en-gb/products/machine-learning/)