Does this skill handle deployments in azd-managed Foundry projects?

No. For azd projects (those scaffolded from azd-ai-starter-basic or via azd ai agent init), declare deployments in azure.yaml instead — azd provision will create them through Bicep. Use this skill only for standalone Foundry projects or ad-hoc deployments outside the azd lifecycle.

What happens if I don't specify a project?

The skill checks the PROJECT_RESOURCE_ID environment variable first, then looks for clues in your prompt. If neither exists, it queries your projects and suggests the current one, with a confirmation step before deploying.

Can this skill list or delete existing deployments?

No. This skill creates deployments only. Use the foundry_models_deployments_list MCP tool to list existing deployments, or the Azure portal to delete them.

Does this skill validate quota and SKU support before deploying?

Yes. It queries the model catalog to confirm the model supports your chosen SKU, and checks your subscription's available quota via Azure CLI before presenting any deployment options.

What should I do if I hit a quota limit?

Defer to the quota skill (quota/quota.md) for quota increase requests, usage monitoring, and troubleshooting quota errors.

deploy-model by microsoft

Preset Deployment Workflow - Detailed Implementation

This file contains the full step-by-step bash/PowerShell scripts for preset (optimal region) model deployment. Referenced from the main SKILL.md.

Phase 1: Verify Authentication

Check if user is logged into Azure CLI:

az account show --query "{Subscription:name, User:user.name}" -o table

If not logged in:

az login

Verify subscription is correct:

# List all subscriptions
az account list --query "[].[name,id,state]" -o table

# Set active subscription if needed
az account set --subscription <subscription-id>

Phase 2: Get Current Project

Check for PROJECT_RESOURCE_ID environment variable first:

if [ -n "$PROJECT_RESOURCE_ID" ]; then
  echo "Using project resource ID from environment: $PROJECT_RESOURCE_ID"
else
  echo "PROJECT_RESOURCE_ID not set. Please provide your Microsoft Foundry project resource ID."
  echo ""
  echo "You can find this in:"
  echo "  • Microsoft Foundry portal → Project → Overview → Resource ID"
  echo "  • Format: /subscriptions/{sub-id}/resourceGroups/{rg}/providers/Microsoft.CognitiveServices/accounts/{account}/projects/{project}"
  echo ""
  echo "Example: /subscriptions/abc123.../resourceGroups/rg-prod/providers/Microsoft.CognitiveServices/accounts/my-account/projects/my-project"
  echo ""
  read -p "Enter project resource ID: " PROJECT_RESOURCE_ID
fi

Parse the ARM resource ID to extract components:

# Extract components from ARM resource ID
# Format: /subscriptions/{sub-id}/resourceGroups/{rg}/providers/Microsoft.CognitiveServices/accounts/{account}/projects/{project}

SUBSCRIPTION_ID=$(echo "$PROJECT_RESOURCE_ID" | sed -n 's|.*/subscriptions/\([^/]*\).*|\1|p')
RESOURCE_GROUP=$(echo "$PROJECT_RESOURCE_ID" | sed -n 's|.*/resourceGroups/\([^/]*\).*|\1|p')
ACCOUNT_NAME=$(echo "$PROJECT_RESOURCE_ID" | sed -n 's|.*/accounts/\([^/]*\)/projects.*|\1|p')
PROJECT_NAME=$(echo "$PROJECT_RESOURCE_ID" | sed -n 's|.*/projects/\([^/?]*\).*|\1|p')

if [ -z "$SUBSCRIPTION_ID" ] || [ -z "$RESOURCE_GROUP" ] || [ -z "$ACCOUNT_NAME" ] || [ -z "$PROJECT_NAME" ]; then
  echo "❌ Invalid project resource ID format"
  echo "Expected format: /subscriptions/{sub-id}/resourceGroups/{rg}/providers/Microsoft.CognitiveServices/accounts/{account}/projects/{project}"
  exit 1
fi

echo "Parsed project details:"
echo "  Subscription: $SUBSCRIPTION_ID"
echo "  Resource Group: $RESOURCE_GROUP"
echo "  Account: $ACCOUNT_NAME"
echo "  Project: $PROJECT_NAME"

Verify the project exists and get its region:

# Set active subscription
az account set --subscription "$SUBSCRIPTION_ID"

# Get project details to verify it exists and extract region
PROJECT_REGION=$(az cognitiveservices account show \
  --name "$PROJECT_NAME" \
  --resource-group "$RESOURCE_GROUP" \
  --query location -o tsv 2>/dev/null)

if [ -z "$PROJECT_REGION" ]; then
  echo "❌ Project '$PROJECT_NAME' not found in resource group '$RESOURCE_GROUP'"
  echo ""
  echo "Please verify the resource ID is correct."
  echo ""
  echo "List available projects:"
  echo "  az cognitiveservices account list --query \"[?kind=='AIProject'].{Name:name, Location:location, ResourceGroup:resourceGroup}\" -o table"
  exit 1
fi

echo "✓ Project found"
echo "  Region: $PROJECT_REGION"

Phase 3: Get Model Name

If model name provided as skill parameter, skip this phase.

Ask user which model to deploy. Fetch available models dynamically from the account rather than using a hardcoded list:

# List available models in the account
az cognitiveservices account list-models \
  --name "$PROJECT_NAME" \
  --resource-group "$RESOURCE_GROUP" \
  --query "[].name" -o tsv | sort -u

Present the results to the user and let them choose, or enter a custom model name.

Store model:

MODEL_NAME="<selected-model>"

Get model version (latest stable):

# List available models and versions in the account
az cognitiveservices account list-models \
  --name "$PROJECT_NAME" \
  --resource-group "$RESOURCE_GROUP" \
  --query "[?name=='$MODEL_NAME'].{Name:name, Version:version, Format:format}" \
  -o table

Use latest version or let user specify:

MODEL_VERSION="<version-or-latest>"

Detect model format:

# Get model format from model catalog (e.g., OpenAI, Anthropic, Meta-Llama, Mistral, Cohere)
MODEL_FORMAT=$(az cognitiveservices account list-models \
  --name "$ACCOUNT_NAME" \
  --resource-group "$RESOURCE_GROUP" \
  --query "[?name=='$MODEL_NAME'].format" -o tsv | head -1)

# Default to OpenAI if not found
MODEL_FORMAT=${MODEL_FORMAT:-"OpenAI"}

echo "Model format: $MODEL_FORMAT"

💡 Model format determines the deployment path:

OpenAI — Standard CLI deployment, TPM-based capacity, RAI policies apply

Anthropic — REST API deployment with modelProviderData, capacity=1, no RAI

All other formats (Meta-Llama, Mistral, Cohere, etc.) — Standard CLI deployment, capacity=1 (MaaS), no RAI

Phase 4: Check Current Region Capacity

Before checking other regions, see if the current project's region has capacity:

# Query capacity for current region
CAPACITY_JSON=$(az rest --method GET \
  --url "https://management.azure.com/subscriptions/$SUBSCRIPTION_ID/providers/Microsoft.CognitiveServices/locations/$PROJECT_REGION/modelCapacities?api-version=2024-10-01&modelFormat=$MODEL_FORMAT&modelName=$MODEL_NAME&modelVersion=$MODEL_VERSION")

# Extract available capacity for GlobalStandard SKU
CURRENT_CAPACITY=$(echo "$CAPACITY_JSON" | jq -r '.value[] | select(.properties.skuName=="GlobalStandard") | .properties.availableCapacity')

Check result:

if [ -n "$CURRENT_CAPACITY" ] && [ "$CURRENT_CAPACITY" -gt 0 ]; then
  echo "✓ Current region ($PROJECT_REGION) has capacity: $CURRENT_CAPACITY TPM"
  echo "Proceeding with deployment..."
  # Skip to Phase 7 (Deploy)
else
  echo "⚠ Current region ($PROJECT_REGION) has no available capacity"
  echo "Checking alternative regions..."
  # Continue to Phase 5
fi

Phase 5: Query Multi-Region Capacity (If Needed)

Only execute this phase if current region has no capacity.

Query capacity across all regions:

# Get capacity for all regions in subscription
ALL_REGIONS_JSON=$(az rest --method GET \
  --url "https://management.azure.com/subscriptions/$SUBSCRIPTION_ID/providers/Microsoft.CognitiveServices/modelCapacities?api-version=2024-10-01&modelFormat=$MODEL_FORMAT&modelName=$MODEL_NAME&modelVersion=$MODEL_VERSION")

# Save to file for processing
echo "$ALL_REGIONS_JSON" > /tmp/capacity_check.json

Parse and categorize regions:

# Extract available regions (capacity > 0)
AVAILABLE_REGIONS=$(jq -r '.value[] | select(.properties.skuName=="GlobalStandard" and .properties.availableCapacity > 0) | "\(.location)|\(.properties.availableCapacity)"' /tmp/capacity_check.json)

# Extract unavailable regions (capacity = 0 or undefined)
UNAVAILABLE_REGIONS=$(jq -r '.value[] | select(.properties.skuName=="GlobalStandard" and (.properties.availableCapacity == 0 or .properties.availableCapacity == null)) | "\(.location)|0"' /tmp/capacity_check.json)

Format and display regions:

# Format capacity (e.g., 120000 -> 120K)
format_capacity() {
  local capacity=$1
  if [ "$capacity" -ge 1000000 ]; then
    echo "$(awk "BEGIN {printf \"%.1f\", $capacity/1000000}")M TPM"
  elif [ "$capacity" -ge 1000 ]; then
    echo "$(awk "BEGIN {printf \"%.0f\", $capacity/1000}")K TPM"
  else
    echo "$capacity TPM"
  fi
}

echo ""
echo "⚠ No Capacity in Current Region"
echo ""
echo "The current project's region ($PROJECT_REGION) does not have available capacity for $MODEL_NAME."
echo ""
echo "Available Regions (with capacity):"
echo ""

# Display available regions with formatted capacity
echo "$AVAILABLE_REGIONS" | while IFS='|' read -r region capacity; do
  formatted_capacity=$(format_capacity "$capacity")
  # Get region display name (capitalize and format)
  region_display=$(echo "$region" | sed 's/\([a-z]\)\([a-z]*\)/\U\1\L\2/g; s/\([a-z]\)\([0-9]\)/\1 \2/g')
  echo "  • $region_display - $formatted_capacity"
done

echo ""
echo "Unavailable Regions:"
echo ""

# Display unavailable regions
echo "$UNAVAILABLE_REGIONS" | while IFS='|' read -r region capacity; do
  region_display=$(echo "$region" | sed 's/\([a-z]\)\([a-z]*\)/\U\1\L\2/g; s/\([a-z]\)\([0-9]\)/\1 \2/g')
  if [ "$capacity" = "0" ]; then
    echo "  ✗ $region_display (Insufficient quota - 0 TPM available)"
  else
    echo "  ✗ $region_display (Model not supported)"
  fi
done

Handle no capacity anywhere:

if [ -z "$AVAILABLE_REGIONS" ]; then
  echo ""
  echo "❌ No Available Capacity in Any Region"
  echo ""
  echo "No regions have available capacity for $MODEL_NAME with GlobalStandard SKU."
  echo ""
  echo "Next Steps:"
  echo "1. Request quota increase — use the quota skill (../../../quota/quota.md)"
  echo ""
  echo "2. Check existing deployments (may be using quota):"
  echo "   az cognitiveservices account deployment list \\"
  echo "     --name $PROJECT_NAME \\"
  echo "     --resource-group $RESOURCE_GROUP"
  echo ""
  echo "3. Consider alternative models with lower capacity requirements:"
  echo "   • gpt-4o-mini (cost-effective, lower capacity requirements)"
  echo "   List available models: az cognitiveservices account list-models --name \$PROJECT_NAME --resource-group \$RESOURCE_GROUP --output table"
  exit 1
fi

Phase 6: Select Region and Project

Ask user to select region from available options.

Example using AskUserQuestion:

Present available regions as options
Show capacity for each
User selects preferred region

Store selection:

SELECTED_REGION="<user-selected-region>"  # e.g., "eastus2"

Find projects in selected region:

PROJECTS_IN_REGION=$(az cognitiveservices account list \
  --query "[?kind=='AIProject' && location=='$SELECTED_REGION'].{Name:name, ResourceGroup:resourceGroup}" \
  --output json)

PROJECT_COUNT=$(echo "$PROJECTS_IN_REGION" | jq '. | length')

if [ "$PROJECT_COUNT" -eq 0 ]; then
  echo "No projects found in $SELECTED_REGION"
  echo "Would you like to create a new project? (yes/no)"
  # If yes, continue to project creation
  # If no, exit or select different region
else
  echo "Projects in $SELECTED_REGION:"
  echo "$PROJECTS_IN_REGION" | jq -r '.[] | "  • \(.Name) (\(.ResourceGroup))"'
  echo ""
  echo "Select a project or create new project"
fi

Option A: Use existing project

PROJECT_NAME="<selected-project-name>"
RESOURCE_GROUP="<resource-group>"

Option B: Create new project

# Generate project name
USER_ALIAS=$(az account show --query user.name -o tsv | cut -d'@' -f1 | tr '.' '-')
RANDOM_SUFFIX=$(openssl rand -hex 2)
NEW_PROJECT_NAME="${USER_ALIAS}-aiproject-${RANDOM_SUFFIX}"

# Prompt for resource group
echo "Resource group for new project:"
echo "  1. Use existing resource group: $RESOURCE_GROUP"
echo "  2. Create new resource group"

# If existing resource group
NEW_RESOURCE_GROUP="$RESOURCE_GROUP"

# Create AI Services account (hub)
HUB_NAME="${NEW_PROJECT_NAME}-hub"

echo "Creating AI Services hub: $HUB_NAME in $SELECTED_REGION..."

az cognitiveservices account create \
  --name "$HUB_NAME" \
  --resource-group "$NEW_RESOURCE_GROUP" \
  --location "$SELECTED_REGION" \
  --kind "AIServices" \
  --sku "S0" \
  --yes

# Create Microsoft Foundry project
echo "Creating Microsoft Foundry project: $NEW_PROJECT_NAME..."

az cognitiveservices account create \
  --name "$NEW_PROJECT_NAME" \
  --resource-group "$NEW_RESOURCE_GROUP" \
  --location "$SELECTED_REGION" \
  --kind "AIProject" \
  --sku "S0" \
  --yes

echo "✓ Project created successfully"
PROJECT_NAME="$NEW_PROJECT_NAME"
RESOURCE_GROUP="$NEW_RESOURCE_GROUP"

Phase 7: Deploy Model

Generate unique deployment name:

The deployment name should match the model name (e.g., "gpt-4o"), but if a deployment with that name already exists, append a numeric suffix (e.g., "gpt-4o-2", "gpt-4o-3"). This follows the same UX pattern as Microsoft Foundry portal.

Use the generate_deployment_name script to check existing deployments and generate a unique name:

Bash version:

DEPLOYMENT_NAME=$(bash scripts/generate_deployment_name.sh \
  "$ACCOUNT_NAME" \
  "$RESOURCE_GROUP" \
  "$MODEL_NAME")

echo "Generated deployment name: $DEPLOYMENT_NAME"

PowerShell version:

$DEPLOYMENT_NAME = & .\scripts\generate_deployment_name.ps1 `
  -AccountName $ACCOUNT_NAME `
  -ResourceGroup $RESOURCE_GROUP `
  -ModelName $MODEL_NAME

Write-Host "Generated deployment name: $DEPLOYMENT_NAME"

Calculate deployment capacity:

Follow UX capacity calculation logic. For OpenAI models, use 50% of available capacity (minimum 50 TPM). For all other models (MaaS), capacity is always 1:

if [ "$MODEL_FORMAT" = "OpenAI" ]; then
  # OpenAI models: TPM-based capacity (50% of available, minimum 50)
  SELECTED_CAPACITY=$(echo "$ALL_REGIONS_JSON" | jq -r ".value[] | select(.location==\"$SELECTED_REGION\" and .properties.skuName==\"GlobalStandard\") | .properties.availableCapacity")

  if [ "$SELECTED_CAPACITY" -gt 50 ]; then
    DEPLOY_CAPACITY=$((SELECTED_CAPACITY / 2))
    if [ "$DEPLOY_CAPACITY" -lt 50 ]; then
      DEPLOY_CAPACITY=50
    fi
  else
    DEPLOY_CAPACITY=$SELECTED_CAPACITY
  fi

  echo "Deploying with capacity: $DEPLOY_CAPACITY TPM (50% of available: $SELECTED_CAPACITY TPM)"
else
  # Non-OpenAI models (MaaS): capacity is always 1
  DEPLOY_CAPACITY=1
  echo "MaaS model — deploying with capacity: 1 (pay-per-token billing)"
fi

If MODEL_FORMAT is NOT "Anthropic" — Standard CLI Deployment

💡 Note: The Azure CLI supports all non-Anthropic model formats directly.

Bash version:

echo "Creating deployment..."

az cognitiveservices account deployment create \
  --name "$ACCOUNT_NAME" \
  --resource-group "$RESOURCE_GROUP" \
  --deployment-name "$DEPLOYMENT_NAME" \
  --model-name "$MODEL_NAME" \
  --model-version "$MODEL_VERSION" \
  --model-format "$MODEL_FORMAT" \
  --sku-name "GlobalStandard" \
  --sku-capacity "$DEPLOY_CAPACITY"

PowerShell version:

Write-Host "Creating deployment..."

az cognitiveservices account deployment create `
  --name $ACCOUNT_NAME `
  --resource-group $RESOURCE_GROUP `
  --deployment-name $DEPLOYMENT_NAME `
  --model-name $MODEL_NAME `
  --model-version $MODEL_VERSION `
  --model-format $MODEL_FORMAT `
  --sku-name "GlobalStandard" `
  --sku-capacity $DEPLOY_CAPACITY

💡 Note: For non-OpenAI MaaS models (Meta-Llama, Mistral, Cohere, etc.), $DEPLOY_CAPACITY is 1 (set in capacity calculation above).

If MODEL_FORMAT is "Anthropic" — REST API Deployment with modelProviderData

The Azure CLI does not support --model-provider-data. You must use the ARM REST API directly.

Step 1: Prompt user to select industry

Present the following list and ask the user to choose one:

 1. None                    (API value: none)
 2. Biotechnology           (API value: biotechnology)
 3. Consulting              (API value: consulting)
 4. Education               (API value: education)
 5. Finance                 (API value: finance)
 6. Food & Beverage         (API value: food_and_beverage)
 7. Government              (API value: government)
 8. Healthcare              (API value: healthcare)
 9. Insurance               (API value: insurance)
10. Law                     (API value: law)
11. Manufacturing           (API value: manufacturing)
12. Media                   (API value: media)
13. Nonprofit               (API value: nonprofit)
14. Technology              (API value: technology)
15. Telecommunications      (API value: telecommunications)
16. Sport & Recreation      (API value: sport_and_recreation)
17. Real Estate             (API value: real_estate)
18. Retail                  (API value: retail)
19. Other                   (API value: other)

⚠️ Do NOT pick a default industry or hardcode a value. Always ask the user. This is required by Anthropic's terms of service. The industry list is static — there is no REST API that provides it.

Store selection as SELECTED_INDUSTRY (use the API value, e.g., technology).

Step 2: Fetch tenant info (country code and organization name)

TENANT_INFO=$(az rest --method GET \
  --url "https://management.azure.com/tenants?api-version=2024-11-01" \
  --query "value[0].{countryCode:countryCode, displayName:displayName}" -o json)

COUNTRY_CODE=$(echo "$TENANT_INFO" | jq -r '.countryCode')
ORG_NAME=$(echo "$TENANT_INFO" | jq -r '.displayName')

PowerShell version:

$tenantInfo = az rest --method GET `
  --url "https://management.azure.com/tenants?api-version=2024-11-01" `
  --query "value[0].{countryCode:countryCode, displayName:displayName}" -o json | ConvertFrom-Json

$countryCode = $tenantInfo.countryCode
$orgName = $tenantInfo.displayName

Step 3: Deploy via ARM REST API

Bash version:

echo "Creating Anthropic model deployment via REST API..."

az rest --method PUT \
  --url "https://management.azure.com/subscriptions/$SUBSCRIPTION_ID/resourceGroups/$RESOURCE_GROUP/providers/Microsoft.CognitiveServices/accounts/$ACCOUNT_NAME/deployments/$DEPLOYMENT_NAME?api-version=2024-10-01" \
  --body "{
    \"sku\": {
      \"name\": \"GlobalStandard\",
      \"capacity\": 1
    },
    \"properties\": {
      \"model\": {
        \"format\": \"Anthropic\",
        \"name\": \"$MODEL_NAME\",
        \"version\": \"$MODEL_VERSION\"
      },
      \"modelProviderData\": {
        \"industry\": \"$SELECTED_INDUSTRY\",
        \"countryCode\": \"$COUNTRY_CODE\",
        \"organizationName\": \"$ORG_NAME\"
      }
    }
  }"

PowerShell version:

Write-Host "Creating Anthropic model deployment via REST API..."

$body = @{
    sku = @{
        name = "GlobalStandard"
        capacity = 1
    }
    properties = @{
        model = @{
            format = "Anthropic"
            name = $MODEL_NAME
            version = $MODEL_VERSION
        }
        modelProviderData = @{
            industry = $SELECTED_INDUSTRY
            countryCode = $countryCode
            organizationName = $orgName
        }
    }
} | ConvertTo-Json -Depth 5

az rest --method PUT `
  --url "https://management.azure.com/subscriptions/$SUBSCRIPTION_ID/resourceGroups/$RESOURCE_GROUP/providers/Microsoft.CognitiveServices/accounts/$ACCOUNT_NAME/deployments/${DEPLOYMENT_NAME}?api-version=2024-10-01" `
  --body $body

💡 Note: Anthropic models use capacity: 1 (MaaS billing model), not TPM-based capacity.

Monitor deployment progress:

echo "Monitoring deployment status..."

MAX_WAIT=300  # 5 minutes
ELAPSED=0
INTERVAL=10

while [ $ELAPSED -lt $MAX_WAIT ]; do
  STATUS=$(az cognitiveservices account deployment show \
    --name "$ACCOUNT_NAME" \
    --resource-group "$RESOURCE_GROUP" \
    --deployment-name "$DEPLOYMENT_NAME" \
    --query "properties.provisioningState" -o tsv 2>/dev/null)

  case "$STATUS" in
    "Succeeded")
      echo "✓ Deployment successful!"
      break
      ;;
    "Failed")
      echo "❌ Deployment failed"
      # Get error details
      az cognitiveservices account deployment show \
        --name "$ACCOUNT_NAME" \
        --resource-group "$RESOURCE_GROUP" \
        --deployment-name "$DEPLOYMENT_NAME" \
        --query "properties"
      exit 1
      ;;
    "Creating"|"Accepted"|"Running")
      echo "Status: $STATUS... (${ELAPSED}s elapsed)"
      sleep $INTERVAL
      ELAPSED=$((ELAPSED + INTERVAL))
      ;;
    *)
      echo "Unknown status: $STATUS"
      sleep $INTERVAL
      ELAPSED=$((ELAPSED + INTERVAL))
      ;;
  esac
done

if [ $ELAPSED -ge $MAX_WAIT ]; then
  echo "⚠ Deployment timeout after ${MAX_WAIT}s"
  echo "Check status manually:"
  echo "  az cognitiveservices account deployment show \\"
  echo "    --name $ACCOUNT_NAME \\"
  echo "    --resource-group $RESOURCE_GROUP \\"
  echo "    --deployment-name $DEPLOYMENT_NAME"
  exit 1
fi

Phase 8: Display Deployment Details

Show deployment information:

echo ""
echo "═══════════════════════════════════════════"
echo "✓ Deployment Successful!"
echo "═══════════════════════════════════════════"
echo ""

# Get endpoint information
ENDPOINT=$(az cognitiveservices account show \
  --name "$ACCOUNT_NAME" \
  --resource-group "$RESOURCE_GROUP" \
  --query "properties.endpoint" -o tsv)

# Get deployment details
DEPLOYMENT_INFO=$(az cognitiveservices account deployment show \
  --name "$ACCOUNT_NAME" \
  --resource-group "$RESOURCE_GROUP" \
  --deployment-name "$DEPLOYMENT_NAME" \
  --query "properties.model")

echo "Deployment Name: $DEPLOYMENT_NAME"
echo "Model: $MODEL_NAME"
echo "Version: $MODEL_VERSION"
echo "Region: $SELECTED_REGION"
echo "SKU: GlobalStandard"
echo "Capacity: $(format_capacity $DEPLOY_CAPACITY)"
echo "Endpoint: $ENDPOINT"
echo ""

# Generate direct link to deployment in Microsoft Foundry portal
DEPLOYMENT_URL=$(bash "$(dirname "$0")/scripts/generate_deployment_url.sh" \
  --subscription "$SUBSCRIPTION_ID" \
  --resource-group "$RESOURCE_GROUP" \
  --foundry-resource "$ACCOUNT_NAME" \
  --project "$PROJECT_NAME" \
  --deployment "$DEPLOYMENT_NAME")

echo "🔗 View in Microsoft Foundry Portal:"
echo ""
echo "$DEPLOYMENT_URL"
echo ""
echo "═══════════════════════════════════════════"
echo ""

echo "Test your deployment:"
echo ""
echo "# View deployment details"
echo "az cognitiveservices account deployment show \\"
echo "  --name $ACCOUNT_NAME \\"
echo "  --resource-group $RESOURCE_GROUP \\"
echo "  --deployment-name $DEPLOYMENT_NAME"
echo ""
echo "# List all deployments"
echo "az cognitiveservices account deployment list \\"
echo "  --name $ACCOUNT_NAME \\"
echo "  --resource-group $RESOURCE_GROUP \\"
echo "  --output table"
echo ""

echo "Next steps:"
echo "• Click the link above to test in Microsoft Foundry playground"
echo "• Integrate into your application"
echo "• Set up monitoring and alerts"

/deploy-model

presetreferencespreset-workflow.md

Preset Deployment Workflow - Detailed Implementation

Phase 1: Verify Authentication

Phase 2: Get Current Project

Phase 3: Get Model Name

Phase 4: Check Current Region Capacity

Phase 5: Query Multi-Region Capacity (If Needed)

Phase 6: Select Region and Project

Phase 7: Deploy Model

If MODEL_FORMAT is NOT "Anthropic" — Standard CLI Deployment

If MODEL_FORMAT is "Anthropic" — REST API Deployment with modelProviderData

Phase 8: Display Deployment Details

Third-party checks

Provenance

Capability

Topics

What it does

Frequently asked

/deploy-model

deploy-modelpresetreferencespreset-workflow.md

Preset Deployment Workflow - Detailed Implementation

Phase 1: Verify Authentication

Phase 2: Get Current Project

Phase 3: Get Model Name

Phase 4: Check Current Region Capacity

Phase 5: Query Multi-Region Capacity (If Needed)

Phase 6: Select Region and Project

Phase 7: Deploy Model

If MODEL_FORMAT is NOT "Anthropic" — Standard CLI Deployment

If MODEL_FORMAT is "Anthropic" — REST API Deployment with modelProviderData

Phase 8: Display Deployment Details

Third-party checks

Provenance

Capability

Topics

README badge

What it does

Frequently asked

Related skills

presetreferencespreset-workflow.md