Real-World Deployment of Azure OpenAI with AI Central

Welcome to our comprehensive guide on the real-world deployment of Azure OpenAI using AI Central.

As AI technologies continue to evolve, the ability to effectively deploy, manage, and optimize AI services becomes increasingly critical. This guide is based on insights shared during a recent webinar hosted by experts from 2bcloud, a leading global managed service provider (MSP).

In today’s rapidly advancing technological landscape, organizations face numerous challenges when integrating AI services into their operations. These challenges include monitoring service usage, managing rate limits, ensuring robust security measures, and handling fallback mechanisms. This guide addresses these challenges by exploring the deployment of Azure OpenAI through AI Central, a smart reverse proxy designed to streamline and enhance the management of AI services.

AI Central is a proprietary solution that provides a robust, configurable pipeline and balancer for managing Azure OpenAI services. It integrates seamlessly with Microsoft Azure services, offering features that address common deployment challenges and optimize performance.

Whether you are a cloud architect, solution architect, DevOps engineer, or IT professional, this guide aims to equip you with the knowledge and tools needed to effectively deploy and manage AI services on Azure. We will walk you through the essential steps, configurations, and best practices, ensuring you can leverage the full potential of Azure OpenAI in your organization.

By the end of this guide, you will have a thorough understanding of the benefits of using AI Central for your AI deployments, practical deployment scenarios, and how to monitor and analyze your AI services effectively. Let’s dive into the world of Azure OpenAI and discover how AI Central can transform your deployment experience.

Overview

This guide aims to help users understand the deployment of Azure OpenAI services through AI Central, addressing common challenges and demonstrating practical scenarios.

AI Central Introduction

AI Central, provides a robust, configurable pipeline for managing Azure OpenAI services, ensuring security, governance, and efficient resource utilization. It addresses challenges such as monitoring usage, managing rate limits, and handling fallback mechanisms, making it an essential tool for any organization deploying AI services on Azure.

By leveraging AI Central, organizations can effectively manage their AI deployments on Azure, addressing common challenges and optimizing performance. The practical scenarios and deployment steps outlined in this guide demonstrate how to configure and manage these services effectively, ensuring optimal performance and resource utilization.

The integration of AI Central into your AI deployment strategy not only simplifies the process but also enhances the overall efficiency and reliability of your AI applications. With features like token-based rate limiting and container deployment, AI Central ensures that your AI services are scalable, secure, and well-governed.

As you implement the insights and practices shared in this guide, you will be better equipped to address the complexities of AI service management and unlock the full potential of Azure OpenAI. Effective AI deployment is a continuous journey, and staying informed about the latest tools and techniques is key to maintaining a competitive edge.

Challenges with OpenAI Services

Deploying OpenAI services poses several challenges, such as:

• Monitoring and logging OpenAI service usage
• Managing rate limit
• Ensuring authorization and security
• Handling fallback mechanisms for errors

AI Central Solution
AI Central addresses these challenges by acting as a smart reverse proxy that provides the following capabilities:

• Monitor and log streaming quota usage
• Prioritize PTU based AOAI and fallback to PAYG
• Balancing between multiple AOAI servers
• Handle Open AI rate-limiting errors
• Enforce rate limiting to a backend AI service
• Create group of AOAI services as a single endpoint, for a seamless shift to PTU

Deployment Scenario: App Service Deployment
AI Central can be deployed as a Docker container within an Azure App Service, integrated with private endpoints, and managed identities.

Practical Demo

Deployment Steps

1. Preparation: Set up a Bicep file with configuration details for deploying the necessary Azure resources.
2. Execution: Deploy the application using Docker to an Azure App Service.
3. Configuration: Integrate the App Service with virtual networks and configure private endpoints.
4. Testing: Use a Python script to test the deployment and ensure the endpoints are correctly routing requests to the AI models.

Key Components

• Bicep File: Contains configurations for OpenAI services, Log Analytics workspace, Key Vault, virtual networks, and managed identities.
• App Service Configuration: Integrate an Azure App Service with network settings and application insights.
• Local Deployment: Run a local container to simulate deployment scenarios and test endpoint configurations.
• Monitoring and Analytics

Azure Monitor
Use Azure Monitor to track:

• Request duration
• Token usage by customer
• Deployment analytics

Application Insights
Users can monitor application performance and logs through integrated Application Insights for detailed diagnostics and metrics.

Summary

Deploying Azure OpenAI with AI Central, demonstrated and supported by 2bcloud, offers a robust solution to the common challenges associated with managing AI services. Throughout this guide, we have explored the capabilities and benefits of AI Central, including its ability to provide intelligent routing, custom authorization, built-in circuit breakers, and comprehensive monitoring tools.

We hope this guide has provided you with valuable knowledge and practical steps to enhance your AI deployment strategy. For further questions and a deeper dive into specific topics, we encourage you to refer to the recorded webinar available on the Microsoft Reactor channel. Thank you for joining us on this journey, and we wish you success in your AI deployment endeavors.

*** A big thanks to Hemant Javeri, 2bcloud’s Global Head of Engineering, for his significant contribution to the webinar and this guide. ***