Expert knowledge for Azure AI Custom Vision development including best practices, decision making, limits & quotas, security, integrations & coding patterns, and deployment. Use when exporting Custom Vision models, calling prediction APIs, using ONNX/TensorFlow, managing CMK/RBAC, or Smart Labeler, and other Azure AI Custom Vision related development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI services (use microsoft-foundry-tools), Azure Machine Learning (use azure-machine-learning), Azure AI Foundry Local (use microsoft-foundry-local).
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Use azure-custom-vision to provide current, documentation-backed guidance for [Custom Vision task], including [deployment or integration target] and [security requirements]; first refresh the skill because its generated documentation is over three months old.
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[Custom Vision task]
[deployment or integration target]
[security requirements]
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SKILL.md
name: azure-custom-visiondescription: Expert knowledge for Azure AI Custom Vision development including best practices, decision making, limits & quotas, security, integrations & coding patterns, and deployment. Use when exporting Custom Vision models, calling prediction APIs, using ONNX/TensorFlow, managing CMK/RBAC, or Smart Labeler, and other Azure AI Custom Vision related development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI services (use microsoft-foundry-tools), Azure Machine Learning (use azure-machine-learning), Azure AI Foundry Local (use microsoft-foundry-local).compatibility: Requires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or fetch_webpage to retrieve documentation.metadata: generated_at: "2026-06-21" generator: "docs2skills/1.0.0"
Azure AI Custom Vision Skill
This skill provides expert guidance for Azure AI Custom Vision. Covers best practices, decision making, limits & quotas, security, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.
How to Use This Skill
IMPORTANT for Agent: Use the Category Index below to locate relevant sections. For categories with line ranges (e.g., L35-L120), use read_file with the specified lines. For categories with file links (e.g., [security.md](security.md)), use read_file on the linked reference file
IMPORTANT for Agent: If metadata.generated_at is more than 3 months old, suggest the user pull the latest version from the repository. If mcp_microsoftdocs tools are not available, suggest the user install it: Installation Guide
This skill requires network access to fetch documentation content:
Preferred: Use mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.
Fallback: Use fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.
Category Index
Category
Lines
Description
Best Practices
L34-L39
Improving Custom Vision model quality with better data collection/labeling strategies and using Smart Labeler to speed and automate image annotation
Decision Making
L40-L45
Guidance on selecting the best Custom Vision domain for your scenario and planning migrations from Custom Vision to other Azure or third‑party vision services.
Limits & Quotas
L46-L50
Details on Custom Vision usage limits per pricing tier, including training/prediction quotas, project and image caps, and how limits affect model training and deployment.
Security
L51-L57
Managing Custom Vision security: encryption with customer-managed keys, secure data handling/export/deletion, and configuring Azure RBAC roles and permissions.
Integrations & Coding Patterns
L58-L68
Using Custom Vision models and APIs in apps: exporting via SDK, running ONNX/TensorFlow in Windows ML/Python, calling classification/detection APIs, and integrating with Azure Storage.
Deployment
L69-L73
Deploying Custom Vision models: copying/backing up projects across regions and exporting models for offline, edge, and mobile (TensorFlow, ONNX, iOS/Android) use.
Best Practices
Topic
URL
Apply Custom Vision data strategies to improve models