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: This file may be large. Use the Category Index below to locate relevant sections, then use read_file with specific line ranges (e.g., L136-L144) to read the sections needed for the user's question
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
Decision Making
Limits & Quotas
Security
Integrations & Coding Patterns
Deployment