/azure-machine-learning

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 Azure ML AutoML, online/batch endpoints, Prompt Flow/RAG, vector stores, or MLflow/ONNX deployments, 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: "2026-09-13"
  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 failures and errors across pipelines, AutoML, endpoints, networking, Kubernetes, environments, data access/labeling, prompt flow, and known platform issues. | | Best Practices | L66-L80 | Guidance on optimizing AutoML and training, handling imbalance/overfitting, preparing data, batch/inference performance, monitoring models, and reducing Azure ML compute and cost. | | Decision Making | L81-L107 | Guides for planning Azure ML architecture and migrations: v1→v2 upgrades, workspace/compute/data moves, network isolation, disaster recovery, and generative AI/Prompt Flow to Agent Framework. | | Architecture & Design Patterns | L108-L113 | Designing real-time inference architectures with online endpoints and building RAG solutions using Azure ML vector stores, including deployment, scaling, and integration patterns. | | Limits & Quotas | L114-L123 | Limits, quotas, and availability for Azure ML: regional/sovereign support, VM SKUs, workspace soft delete, and capacity planning for managed online endpoints. | | Security | L124-L173 | 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 | L174-L409 | Configuring Azure ML components, AutoML, compute, networking, monitoring, and YAML/CLI schemas for training, deployment, data, feature stores, prompt flow, and Responsible AI. | | Integrations & Coding Patterns | L410-L453 | Integrating Azure ML with data platforms, REST/MLflow APIs, Spark, Databricks/Synapse/Fabric, and building/debugging prompt flow/RAG tools and deployments. | | Deployment | L454-L483 | Deploying and operationalizing models and pipelines on Azure ML (online/batch endpoints, CI/CD, MLOps, prompt flow, RAG, HF/MLflow/ONNX), including rollout strategies and cross-workspace/registry use. |

Troubleshooting

| Topic | URL | |-------|-----| | Troubleshoot Azure ML designer component error codes | https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/designer-error-codes?view=azureml-api-2 | | Resolve common Azure AutoML forecasting issues | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-automl-forecasting-faq?view=azureml-api-2 | | Debug Azure ML online endpoints locally with VS Code | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-managed-online-endpoints-visual-studio-code?view=azureml-api-2 | | Diagnose and fix Azure ML pipeline failures in studio | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipeline-failure?view=azureml-api-2 | | Troubleshoot Azure ML pipeline performance with profiling | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipeline-performance?view=azureml-api-2 | | Diagnose and fix Azure ML pipeline reuse issues | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipeline-reuse-issues?view=azureml-api-2 | | Troubleshoot Azure automated ML experiment failures | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-auto-ml?view=azureml-api-2 | | Troubleshoot Azure ML batch endpoints and jobs | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-batch-endpoints?view=azureml-api-2 | | Troubleshoot data access issues in Azure ML SDK v2 | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-data-access?view=azureml-api-2 | | Troubleshoot Azure ML data labeling project creation | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-data-labeling?view=azureml-api-2 | | Troubleshoot Azure ML environment