azure-machine-learning

What this skill does

# 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-L71 | Diagnosing and fixing Azure ML errors: pipelines, AutoML, endpoints, networking, Kubernetes, environments, data access, prompt flow, and known issues/workspace diagnostics. |
| Best Practices | L72-L93 | Best practices for Azure ML experiments: model tuning, monitoring, cost and compute optimization, data prep, deployment scripts, GPU/distributed training, and prompt/model performance. |
| Decision Making | L94-L124 | Guidance for architectural and migration decisions in Azure ML: choosing algorithms, training and networking options, cost/DR strategies, and upgrading/migrating from AML v1, Prompt Flow, and legacy features. |
| Architecture & Design Patterns | L125-L131 | Designing Azure ML inference architectures: choosing endpoint types, planning real-time online endpoints, and structuring data movement and multistep pipeline components. |
| Limits & Quotas | L132-L140 | Info on Azure ML regional/sovereign availability, VM SKUs, and service limits, plus how to view, plan, and manage quotas and capacity for model deployments and endpoints. |
| Security | L141-L194 | Securing Azure ML: encryption, keys, identity/RBAC, auth, secrets, network isolation/VNets, data exfil prevention, policy compliance, and securing endpoints, training, RAG, and prompt flows. |
| Configuration | L195-L454 | Configuring Azure ML components, pipelines, compute, networking, monitoring, AutoML, YAML schemas, and data/model management for training, deployment, and responsible/production ML. |
| Integrations & Coding Patterns | L455-L512 | Patterns and how-tos for wiring Azure ML to data/compute (Synapse, Databricks, Fabric, ADF), using MLflow, REST/HTTP, Spark, Prompt Flow, and integrating LLMs, events, and external apps. |
| Deployment | L513-L553 | Deploying and operationalizing models and prompt flows: online/batch endpoints, CI/CD (GitHub/Azure DevOps), blue‑green rollouts, MLOps/GenAIOps, pipelines, and cross-workspace consumption. |

### 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 |
| Troubleshoot ParallelRunStep failures in Azure ML pipelines | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-parallel-run-step?view=azureml-api-1 |
| Debug Azure ML pipeline failures in studio | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipeline-failure?view=azureml-api-2 |
| Diagnose Azure ML pipeline performance issues 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 ML SDK v1 pipelines | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines?view=azureml-api-1 |
| 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 image builds and packages | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-environments?view=azureml-api-2 |
| Troubleshoot Azure ML Kubernetes compute workloads | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-kubernetes-compute?view=azureml-api-2 |
| Troubleshoot Azure ML Kubernetes extension deployment | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-kubernetes-extension?view=azureml-api-2 |
| Diagnose Azure ML managed virtual network issues | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-managed-network?view=azureml-api-2 |
| Diagnose and fix Azure ML online endpoint errors | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-online-endpoints?view=azureml-api-2 |
| Diagnose and fix Azure ML online endpoint errors | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-online-endpoints?view=azureml-api-2 |
| Troubleshoot Azure ML online endpoint deployment and scoring errors | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-online-endpoints?view=azureml-api-2 |
| Troubleshoot Azure ML prebuilt Docker inference images | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-prebuilt-docker-image-inference?view=azureml-api-1 |
| Resolve 'descriptors cannot be created directly' in Azure ML | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-protobuf-descriptor-error?view=azureml-api-2 |
| Troubleshoot Azure ML private endpoint connectivity | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-secure-connection-workspace?view=azureml-api-2 |
| Fix SerializationError import issues in Azure ML SDK v1 | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-serialization-error?view=azureml-api-1 |
| Fix 'Validation for schema failed' errors in Azure ML CLI v2 | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-validation-for-schema-failed-error?view=azureml-api-2 |
| Use Azure ML workspace diagnostics for issues | https://learn.microsoft.com/en-us/azure/machine-learning/how-to-workspace-diagnostic-api?view=azureml-api-2 |
| Review Azure Machine Learning current known issues | https://learn.microsoft.com/en-us/azure/machine-learning/known-issues/azure-machine-learning-known-issues?view=azureml-api-2 |
| Known issue: Invalid certificate during AKS deployment | https://learn.microsoft.