AI-102 Study Series Part 6: Managing Azure OpenAI Models in Azure AI Foundry
Managing Azure OpenAI models in Azure AI Foundry involves several key steps, including deployment, customization, monitoring, and scaling. Here’s a detailed guide: Step 1: Deploy Models Sign in to Azure AI Foundry: Go to the Azure AI Foundry portal and sign in. Select Your Project: Choose the project where you want to deploy the model. Model Catalog: Navigate to the Model Catalog and select the Azure OpenAI model you want to deploy. Deploy Model: Click on “Deploy” and configure the deployment settings, such as resource allocation and endpoint configuration 1. Step 2: Customize Models Open in Playground: After deployment, open the model in the Azure AI Foundry playground. Fine-Tuning: Customize the model with your own data using fine-tuning techniques. This improves the model’s accuracy and relevance for your specific use case 2. Embeddings and Indexes: Integrate additional components like embeddings and indexes to enhance the model’s capabilities 1. Step 3: Monitor and Scale Monitoring: Use Azure Monitor to track the performance and health of your deployed models. Set up alerts for any anomalies or performance issues 2. Scaling: Adjust the resource allocation based on demand. You can scale up or down using Azure AI Foundry’s flexible deployment options, such as serverless, managed, or reserved 2. Step 4: Manage Security and Compliance Security: Implement robust security frameworks to protect your models and data. Use built-in tools to manage harmful content and ensure compliance with regulations 2. Governance: Maintain governance over your AI models by tracking usage and access through Azure’s enterprise-grade security features 2. Example Configuration Here’s an example of deploying a model using the Azure CLI: ...