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	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/getting-started-improving-your-classifier
Speed up Custom Vision labeling with Smart Labeler	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/suggested-tags

Decision Making

Topic	URL
Plan migration from Custom Vision to alternative services	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/migration-options
Choose the right Custom Vision domain for your project	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/select-domain

Limits & Quotas

Topic	URL
Review Custom Vision limits and quotas by tier	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/limits-and-quotas

Security

Topic	URL
Configure customer-managed keys for Custom Vision encryption	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/encrypt-data-at-rest
View, export, and delete Custom Vision data securely	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/export-delete-data
Configure Azure RBAC roles for Custom Vision projects	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/role-based-access-control

Integrations & Coding Patterns

Topic	URL
Integrate Custom Vision ONNX models with Windows ML apps	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/custom-vision-onnx-windows-ml
Run exported Custom Vision TensorFlow models in Python	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/export-model-python
Export Custom Vision models programmatically with SDK	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/export-programmatically
Use Custom Vision SDK for image classification	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/quickstarts/image-classification
Call Custom Vision object detection APIs with SDK	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/quickstarts/object-detection
Integrate Custom Vision with Azure Storage queues and blobs	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/storage-integration
Use Custom Vision prediction API to test images	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/use-prediction-api

Deployment

Topic	URL
Copy and back up Custom Vision projects across regions	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/copy-move-projects
Export Custom Vision models for offline and mobile use	https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/export-your-model

Files1

1 files · 18.2 KB

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Overall Score

81/100

Grade

B

Good

Safety

88

Quality

80

Clarity

85

Completeness

72

Summary

A reference skill providing expert guidance on Azure AI Custom Vision development, covering best practices, decision-making, limits, security, integrations, and deployment. The skill is a curated index linking to Microsoft Learn documentation with fallback network-based retrieval options.

Detected Capabilities

fetch_webpageread_filemcp_microsoftdocs:microsoft_docs_fetchdocumentation retrievalreference lookup

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

custom vision exportazure custom visionvision model deploymentcustom vision domain selectionsmart labeler annotationcustom vision api integrationmigrate custom vision

Referenced Domains

External domains referenced in skill content, detected by static analysis.

github.comlearn.microsoft.com

Use Cases

Exporting Custom Vision models in ONNX or TensorFlow format for offline/mobile deployment
Selecting appropriate domains and planning migrations between Custom Vision and other Azure vision services
Configuring security: customer-managed keys, RBAC roles, and data export/deletion workflows
Integrating Custom Vision classification and object detection APIs into applications
Using Smart Labeler to accelerate image annotation and improve training data quality
Copying and backing up Custom Vision projects across Azure regions for disaster recovery
Calling prediction APIs to evaluate trained models on new images

Quality Notes

Well-structured category index with clear line-range mappings enables efficient navigation for agents
Skill explicitly states network requirements and provides fallback fetch strategies (preferred vs. fallback tools)
Referenced documentation is specific and authoritative (Microsoft Learn URLs)
Clear scope boundaries: excludes overlapping Azure services (AI Vision, Machine Learning, AI Foundry)
Metadata freshness check built in (3-month threshold) with installation guidance for missing MCP tools
Comprehensive topic coverage across six domains (practices, decisions, limits, security, integrations, deployment)
All links are descriptive and current (learn.microsoft.com domain)

Model: claude-haiku-4-5-20251001Analyzed: Jun 26, 2026

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