包阅导读总结
1. `Azure AI Studio`、`Baseline Reference Implementation`、`AI Deployments`、`Features`、`Supported Scenarios`
2. 文本介绍了 Azure AI Studio 旨在满足开发者需求，着重运营卓越，推出了端到端基线参考实现。该架构具有多种重要特性，支持多种场景，能助力组织应对云 AI 部署挑战，链接可获取参考实现。
3.
– Azure AI Studio
– 设计目的：满足开发者将先进 AI 能力集成到应用中的需求，注重运营卓越。
– 解决关键因素：如安全、可扩展性和法规遵循。
– 端到端基线参考实现
– 是促进云 AI 工作负载部署的指南。
– 协助组织找到结构化解决方案。
– 架构特点
– 安全网络边界。
– 身份管理。
– 可扩展性。
– 合规与治理。
– 支持场景
– AI Studio 项目游乐场。
– Promptflow 工作流。
– 弹性、托管部署。
– 利用 Azure App Service 自托管。
– 价值
– 助力组织应对云 AI 部署挑战。
– 促进创新、安全和合规的解决方案。
– 可通过链接获取参考实现。
思维导图：
文章来源：techcommunity.microsoft.com
作者：FreddyAyala
发布时间：2024/8/30 20:21
语言：英文
总字数：379字
预计阅读时间：2分钟
评分：85分
标签：Azure AI 工作室,AI 部署,云架构,安全,可扩展
以下为原文内容
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Azure AI Studio is designed to cater to the growing needs of developers seeking to integrate advanced AI capabilities into their applications with a focus on operational excellence. Addressing key factors such as security, scalability, and regulatory adherence, Azure AI Studio ensures that AI deployments are seamless, sustainable, and strategically aligned with business objectives.
We’re excited to present the end-to-end baseline reference implementation for Azure AI Studio, a definitive guide designed to facilitate the deployment of AI workloads in the cloud. This architecture has been designed to assist organizations in finding structured solutions for deploying AI applications that are production ready in an enterprise environment at scale.
Features of the Baseline Architecture
This architecture incorporates several important features:
- Secure Network Perimeter: Creates a secure boundary for AI applications with strict network security and segmentation capabilities.
- Identity Management: Implements strong access management to regulate interactions and maintain secure operations within AI services and data.
- Scalability: Provides a flexible infrastructure to support the growth of AI applications, ensuring performance is not sacrificed as demand increases.
- Compliance and Governance: Maintains a commitment to following enterprise governance policies and meeting compliance standards throughout the life of an AI application.
Supported Scenarios of the Baseline Architecture
The reference architecture supports various important use cases, including:
- AI Studio Project Playground: An integrated environment for engaging with Azure OpenAI technologies, where you can chat with your data, test out various AI-powered assistants, and utilize completion features for text. This tool serves as a one-stop shop to assess, refine, and validate your AI-driven projects.
- Promptflow Workflows: This feature supports the development of complex AI workflows, integrating elements like custom Python scripts and large language model integrations, enhancing operational excellence.
- Resilient, Managed Deployments: Manages the deployment of AI applications to Azure’s managed virtual networks, ensuring solid and dependable access via client UI hosted in Azure App Service.
- Self-Hosting with Azure App Service: This alternative gives enterprises full control to customize and manage Promptflow deployment using Azure App Service leveraging advanced options such as availability zones.
Taking advantage of the end-to-end baseline reference implementation enables organizations to address the challenges of cloud-based AI deployment, fostering innovation in solutions that are secure and comply with organizational governance and regulations.
You can find the reference implementation in the following link: aistudio-end-to-end-baseline-architecture
