azure-machine-learning

Este extracto es una copia guardada durante la revisión. La fuente externa contiene la versión completa y actual.

---
name: azure-machine-learning
description: Expert knowledge for Azure Machine Learning development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using AutoML, managed online endpoints, Prompt Flow/RAG, feature store, or MLflow integrations, and other Azure Machine Learning related development tasks. Not for Azure Databricks (use azure-databricks), Azure Synapse Analytics (use azure-synapse-analytics), Azure Data Science Virtual Machines (use azure-data-science-vm), Azure HDInsight (use azure-hdinsight).
compatibility: Requires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or fetch_webpage to retrieve documentation.
metadata:
  generated_at: "2026-08-02"
  generator: "docs2skills/1.0.0"
---
# Azure Machine Learning Skill

This skill provides expert guidance for Azure Machine Learning. Covers troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, 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](https://github.com/MicrosoftDocs/mcp/blob/main/README.md)

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 |
|----------|-------|-------------|
| Troubleshooting | L37-L65 | Diagnosing and fixing Azure ML issues: pipelines, endpoints, networking, Kubernetes, environments, AutoML, prompt flow, feature store, and known platform bugs/errors. |
| Best Practices | L66-L82 | Guidance on ML best practices: cost and compute optimization, AutoML tuning, model monitoring, feature engineering, batch scoring, GPU/distributed training, and inference performance. |
| Decision Making | L83-L109 | Guides for planning and decision-making in Azure ML: choosing training/network options, DR/failover, and detailed migration/upgrade paths from v1 to v2, ACI, Prompt Flow, and data/compute assets. |
| Architecture & Design Patterns | L110-L115 | Designing Azure ML inference architectures: choosing endpoint types, planning real-time online endpoints, and structuring data movement and multistep pipeline components. |
| Limits & Quotas | L116-L125 | Limits, quotas, and availability for Azure ML: regional/sovereign support, VM SKUs, workspace soft delete, and capacity planning for managed online endpoints. |
| Security | L126-L175 | Securing Azure ML: encryption, keys, identity/RBAC, policies, network isolation/VNets, private endpoints, DNS, data exfil prevention, and secure access to endpoints, storage, Key Vault, and prompt flows. |
| Configuration | L176-L412 | Configuring Azure ML components, jobs, and infrastructure: AutoML, designer components, YAML schemas, compute, networking, data, monitoring, Responsible AI, and prompt flow setups. |
| Integrations & Coding Patterns | L413-L457 | Patterns and code for integrating Azure ML with data sources, Spark, MLflow, REST/HTTP, Synapse/Databricks/Fabric, Event Grid, and building prompt flow/LLM tools and RAG workflows. |
| Deployment |