Microsoft Reactor Sydney

Deploying your app for the first time can feel overwhelming, with the constant uncertainty of whether you’ve missed a step or misunderstood documentation. It can easily become a frustrating experience.

In this session, we’ll explore how GitHub Copilot for Azure, integrated with Visual Studio Code, can simplify and accelerate your development and deployment process. You’ll learn how to build, deploy, and troubleshoot your app directly within your IDE—without the usual complexity.

GitHub Copilot will guide you step-by-step through the deployment process, using real time, AI-powered prompts that assist with everything from creating basic static websites to more complex applications. The best part? You’ll be able to manage it all without leaving VS Code.

This session is for beginner developers and DevOps professionals alike, providing an easy to use, AI assisted workflow for deploying applications to Azure.

Please register here for this session: https://aka.ms/DeployYourFirstApp

As organizations move from AI experimentation to AI Operationalization, they are hit with several realizations around optimum Token utilization of the Azure Open AI Instances, how to scale and how many AOAI instances to maintain, calculation of chargeback for Azure AI services utilization, rate limiting, observability and monitoring. Also, for large organizations, while experimenting with AI Usecases, there is an overhead cost for creating the required infrastructure and ensuring its compliance with the internal security policies etc.

The AI Factory Platform is a scalable and secure environment designed to support the development, deployment, and management of AI solutions across our client's organization. This platform enables application developers to request approved AI services, look at shared dashboards for utilization metrics, rate limits and application chargeback costs.

There are also routing mechanisms implemented to ensure graceful failover from PTU to PAYG instances, retry with backoff of certain limited Azure Open AI deployments, priority-based routing and weight-based routing with the APIM policies. We also demonstrate how to handle instances scaling, load and traffic management via APIM for busy workloads and how to prevent throttling of Azure OPEN AI instances by chatty applications.

We essentially build an AI control tower for organizations to easily and securely scale and manage their GEN AI workloads in different environments.