Deploy Secure Azure AI Studio with a Managed Virtual Network
This article and companion sample demonstrates how to set up an Azure AI Studio environment with managed identity and Azure RBAC to connected Azure AI Services and dependent resources and with the managed virtual network isolation mode set to Allow Internet Outbound. For more information, see How to configure a managed network for Azure AI Studio hubs.
01 Getting Started with Data in Azure Machine Learning
Welcome to our exploration of data management within Azure Machine Learning (AML). This blog serves as a practical guide for those looking to harness the full potential of AML's data handling capabilities. From secure datastores to seamless integration with various storage solutions, we'll navigate the essentials of managing data effectively in the cloud. Key Highlights: Azure Machine Learning Datastore: Introduction to datastores and their integration with storage types like Blob, Files, and ADLS (Azure Data Lake Storage). Data Security and Accessibility: Strategies for secure and efficient data access. External Data Connections: Tips for connecting with sources like Snowflake, Azure SQL DB, and AWS S3. AML Data Assets: Simplifying data access using data assets. MLTable Utilization: Leveraging MLTable for data loading and transformation. Delta Table Insights: Understanding the role and benefits of Delta tables in data lakes.
