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anishi1222
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deploy-model

Deploy Azure OpenAI models with custom version, SKU, capacity, and RAI policy.

Updated Feb 20, 2026
Search Tags:#azure#customization#openai#model deployment#capacity discovery
One-click install
npx skills add https://github.com/anishi1222/multi-agent-code-reviewer --skill deploy-model-anishi1222
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deploy-model
Source: https://github.com/anishi1222/multi-agent-code-reviewer/tree/main/.agents/skills/microsoft-foundry/models/deploy-model
Command: npx skills add https://github.com/anishi1222/multi-agent-code-reviewer --skill deploy-model-anishi1222

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires az, mcp, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates and streamlines the deployment of Azure OpenAI models to the optimal location with the ability to customize deployment parameters to fit specific requirements.

Core Features & Use Cases

  • Automated Deployment: Deploy Azure OpenAI models to the best-suited region with predefined or user-defined parameters.
  • Customization: Customize model deployment with version, SKU, capacity, RAI policy, and advanced options.
  • Use Case: For instance, deploy a GPT-4 model with specific capacity, SKU, and RAI policy for a new project with no existing deployments.

Quick Start

Deploy a GPT-4 model with custom settings to your project by running the command 'deploy-model customize --project my-project --model gpt-4 --version latest --sku GlobalStandard --capacity 50K --rai-policy Custom --config-file my-config.json'.

Frequently Asked Questions about deploy-model

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I deploy an Azure OpenAI model to a specific region with custom SKU and capacity?▼

To deploy an Azure OpenAI model with custom SKU and capacity, use this Skill to specify version, capacity, and RAI policy parameters, automating regional deployment to fit your project requirements.

Can I customize the RAI policy and version when deploying Azure OpenAI models?▼

Yes, you can customize the RAI policy and version for Azure OpenAI model deployment by defining these parameters during the automated deployment configuration process to meet specific project guidelines.

Do I need Azure CLI and MCP tools to discover capacity for OpenAI model deployment?▼

Yes, you need Azure CLI and MCP tools installed to perform capacity discovery and execute automated, customized OpenAI model deployments across optimal Azure regions.

What is the best way to find the optimal region for my Azure OpenAI PTU deployment?▼

The best way to find the optimal region for PTU deployment is using this Skill's capacity discovery feature, which automates region selection based on your specified model version and capacity requirements.

Are there limitations when deploying Azure OpenAI models that require modelProviderData?▼

Yes, a limitation is that certain Azure OpenAI models require modelProviderData to be specified in your configuration, alongside standard parameters like SKU and RAI policy, to ensure successful deployment.

Why does my Azure OpenAI custom deployment require a specific config file?▼

Your Azure OpenAI custom deployment requires a config file to pass advanced options and specific parameters like modelProviderData, ensuring the model is deployed with the correct capacity and regional settings.

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