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), useread_filewith the specified lines. For categories with file links (e.g.,[security.md](security.md)), useread_fileon the linked reference file
IMPORTANT for Agent: If
metadata.generated_atis more than 3 months old, suggest the user pull the latest version from the repository. Ifmcp_microsoftdocstools 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_fetchwith query stringfrom=learn-agent-skill. Returns Markdown. - Fallback: Use
fetch_webpagewith query stringfrom=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-L408 | Configuring Azure ML components, compute, networking, AutoML, YAML schemas, monitoring, and Prompt Flow so you can build, train, deploy, and manage ML workflows and infrastructure. |
| Integrations & Coding Patterns | L409-L451 | Integrating Azure ML with data platforms, REST/MLflow APIs, Spark, Databricks/Synapse/Fabric, and building/debugging prompt flow/RAG tools and deployments. |
| Deployment | L452-L481 | 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
Best Practices
Decision Making
Architecture & Design Patterns
| Topic | URL |
|---|---|
| Plan real-time inference with Azure ML online endpoints | https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints-online?view=azureml-api-2 |
| Use Azure ML vector stores for RAG architectures | https://learn.microsoft.com/en-us/azure/machine-learning/concept-vector-stores?view=azureml-api-2 |
Limits & Quotas
| Topic | URL |
|---|---|
| Check regional availability for standard model deployments | https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoint-serverless-availability?view=azureml-api-2 |
| Understand soft delete retention for ML workspaces | https://learn.microsoft.com/en-us/azure/machine-learning/concept-soft-delete?view=azureml-api-2 |
| Manage Azure ML resource quotas and limits | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-quotas?view=azureml-api-2 |
| Check Azure ML feature availability by sovereign cloud | https://learn.microsoft.com/en-us/azure/machine-learning/reference-machine-learning-cloud-parity?view=azureml-api-2 |
| Supported VM SKUs for Azure ML managed online endpoints | https://learn.microsoft.com/en-us/azure/machine-learning/reference-managed-online-endpoints-vm-sku-list?view=azureml-api-2 |
| Plan capacity with Azure Machine Learning service limits | https://learn.microsoft.com/en-us/azure/machine-learning/resource-limits-capacity?view=azureml-api-2 |