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

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 errors in pipelines, endpoints, environments, networking, Kubernetes, AutoML, prompt flow, feature store, and known platform issues.
Best Practices	L66-L81	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	L82-L108	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	L109-L114	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	L115-L124	Limits, quotas, and availability for Azure ML: regional/sovereign support, VM SKUs, workspace soft delete, and capacity planning for managed online endpoints.
Security	L125-L174	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	L175-L409	Configuring Azure ML: AutoML jobs, designer components, compute, networking, storage, deployments, monitoring, Prompt Flow, and full CLI/YAML schemas for jobs, data, models, and feature stores.
Integrations & Coding Patterns	L410-L454	Integrating Azure ML with data platforms, REST/MLflow APIs, Spark, Databricks/Synapse/Fabric, and building/debugging prompt flow/RAG tools and deployments.
Deployment	L455-L484	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 image build failures	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
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
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 '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
Diagnose and fix Azure ML workspace 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.com/en-us/azure/machine-learning/known-issues/inferencing-invalid-certificate?view=azureml-api-2
Known issue: Updating Azure ML Kubernetes compute fails	https://learn.microsoft.com/en-us/azure/machine-learning/known-issues/inferencing-updating-kubernetes-compute-appears-to-succeed?view=azureml-api-2
Troubleshoot common prompt flow issues in Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/troubleshoot-guidance?view=azureml-api-2
Troubleshoot common prompt flow issues in Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/troubleshoot-guidance?view=azureml-api-2
Troubleshoot Azure ML managed feature store errors	https://learn.microsoft.com/en-us/azure/machine-learning/troubleshooting-managed-feature-store?view=azureml-api-2

Best Practices

Topic	URL
Mitigate overfitting and imbalance in Azure AutoML	https://learn.microsoft.com/en-us/azure/machine-learning/concept-manage-ml-pitfalls?view=azureml-api-2
Understand Azure ML model monitoring concepts and practices	https://learn.microsoft.com/en-us/azure/machine-learning/concept-model-monitoring?view=azureml-api-2
Optimize and manage Azure Machine Learning costs	https://learn.microsoft.com/en-us/azure/machine-learning/concept-plan-manage-cost?view=azureml-api-2
Design feature set transformations in Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/feature-set-specification-transformation-concepts?view=azureml-api-2
Author batch scoring scripts for AML batch deployments	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-batch-scoring-script?view=azureml-api-2
Tune Azure ML Kubernetes inference router performance	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-kubernetes-inference-routing-azureml-fe?view=azureml-api-2
Optimize Azure Machine Learning compute costs	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-optimize-cost?view=azureml-api-2
Prepare image datasets for Azure AutoML vision	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-prepare-datasets-for-automl-images?view=azureml-api-2
Apply distributed GPU training patterns in Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-distributed-gpu?view=azureml-api-2
Evaluate and compare Azure AutoML experiment results	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml?view=azureml-api-2
Optimize AutoML for small object detection in images	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-automl-small-object-detect?view=azureml-api-2
Optimize checkpoint performance for large Azure ML models with Nebula	https://learn.microsoft.com/en-us/azure/machine-learning/reference-checkpoint-performance-for-large-models?view=azureml-api-2

Decision Making

Topic	URL
Choose between managed and custom network isolation in Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/concept-network-isolation-configurations?view=azureml-api-2
Choose migration paths from Azure ML Data Import to Fabric	https://learn.microsoft.com/en-us/azure/machine-learning/data-import-migration-guide?view=azureml-api-2
Plan failover and disaster recovery for Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-high-availability-machine-learning?view=azureml-api-2
Manage and migrate imported data assets in Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-imported-data-assets?view=azureml-api-2
Decide and plan migration from Azure ML v1 to v2	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-migrate-from-v1?view=azureml-api-2
Move Azure ML workspaces between subscriptions	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-move-workspace?view=azureml-api-2
Plan Azure ML network isolation architecture	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-network-isolation-planning?view=azureml-api-2
Map Azure ML v1 datasets to v2 data assets	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-assets-data?view=azureml-api-2
Migrate Azure ML model management from SDK v1 to v2	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-assets-model?view=azureml-api-2
Upgrade Azure ML script runs to v2 command jobs	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-command-job?view=azureml-api-2
Migrate Azure ML deployment endpoints from SDK v1 to v2	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-deploy-endpoints?view=azureml-api-2
Upgrade Azure ML pipeline endpoints from v1 to v2	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-deploy-pipelines?view=azureml-api-2
Upgrade Azure ML AutoML workflows from SDK v1 to v2	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-execution-automl?view=azureml-api-2
Migrate Azure ML hyperparameter tuning to v2 sweep jobs	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-execution-hyperdrive?view=azureml-api-2
Migrate Azure ML parallel run step to SDK v2 parallel job	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-execution-parallel-run-step?view=azureml-api-2
Upgrade Azure ML pipelines from SDK v1 to v2 jobs	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-execution-pipeline?view=azureml-api-2
Migrate Azure ML local runs from SDK v1 to v2	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-local-runs?view=azureml-api-2
Upgrade ACI web services to Azure ML managed online endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-managed-online-endpoints?view=azureml-api-2
Compare and migrate Azure ML compute management v1 to v2	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-resource-compute?view=azureml-api-2
Migrate datastore management from AML v1 to v2	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-resource-datastore?view=azureml-api-2
Decide how to upgrade Azure ML workspaces to SDK v2	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-resource-workspace?view=azureml-api-2
Select and interpret Azure ML generative AI monitoring metrics	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-model-monitoring-generative-ai-evaluation-metrics?view=azureml-api-2
Plan migration from Prompt Flow to Agent Framework	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/migrate-prompt-flow-to-agent-framework?view=azureml-api-2

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

Security

Topic	URL
Configure customer-managed keys for Azure Machine Learning	https://learn.microsoft.com/en-us/azure/machine-learning/concept-customer-managed-keys?view=azureml-api-2
Understand data encryption for Azure ML compute and storage	https://learn.microsoft.com/en-us/azure/machine-learning/concept-data-encryption?view=azureml-api-2
Understand data handling and privacy for Azure ML Model Catalog	https://learn.microsoft.com/en-us/azure/machine-learning/concept-data-privacy?view=azureml-api-2
Understand auth and RBAC for AML online endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints-online-auth?view=azureml-api-2
Plan enterprise security and governance for Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/concept-enterprise-security?view=azureml-api-2
Secret injection concepts for AML online endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/concept-secret-injection?view=azureml-api-2
Understand secure network traffic flow for Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/concept-secure-network-traffic-flow?view=azureml-api-2
Network isolation concepts for AML managed endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/concept-secure-online-endpoint?view=azureml-api-2
Manage vulnerabilities in Azure Machine Learning images	https://learn.microsoft.com/en-us/azure/machine-learning/concept-vulnerability-management?view=azureml-api-2
Configure inbound and outbound traffic for secure Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-azureml-behind-firewall?view=azureml-api-2
Securely connect Azure ML managed VNet to on-premises	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-on-premises-resources?view=azureml-api-2
Access Azure resources from AML endpoints via managed identity	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-resources-from-endpoints-managed-identities?view=azureml-api-2
Grant limited access to Azure ML labeling projects	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-add-users?view=azureml-api-2
Administer data access and authentication for Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-administrate-data-authentication?view=azureml-api-2
Manage Azure ML workspace access with RBAC roles	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-assign-roles?view=azureml-api-2
Authorize access to Azure ML batch endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-batch-endpoint?view=azureml-api-2
Configure authentication for Azure ML online endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-online-endpoint?view=azureml-api-2
Use built-in Azure Policy to govern AI model deployments	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-built-in-policy-model-deployment?view=azureml-api-2
Rotate Azure ML workspace storage access keys securely	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-change-storage-access-key?view=azureml-api-2
Configure custom DNS for private Azure ML endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-custom-dns?view=azureml-api-2
Create custom Azure Policies to restrict AI model deployments	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-custom-policy-model-deployment?view=azureml-api-2
Use secret injection to access secrets in AML deployments	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoint-with-secret-injection?view=azureml-api-2
Disable shared key access for Azure ML workspace storage	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-disable-local-auth-storage?view=azureml-api-2
Configure identity-based service authentication for Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-identity-based-service-authentication?view=azureml-api-2
Enforce Azure ML workspace compliance with Azure Policy	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-integrate-azure-policy?view=azureml-api-2
Configure managed virtual network isolation for Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-managed-network?view=azureml-api-2
Configure Model Catalog access with workspace managed virtual networks	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-network-isolation-model-catalog?view=azureml-api-2
Secure Azure ML workspaces with VNets and isolation	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-network-security-overview?view=azureml-api-2
Configure data exfiltration prevention for Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-prevent-data-loss-exfiltration?view=azureml-api-2
Secure Azure ML batch endpoints with private networks	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-batch-endpoint?view=azureml-api-2
Secure Azure ML online inferencing with VNets	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-inferencing-vnet?view=azureml-api-2
Secure AKS inferencing environments for Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-kubernetes-inferencing-environment?view=azureml-api-2
Configure TLS/SSL for Azure ML Kubernetes endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-kubernetes-online-endpoint?view=azureml-api-2
Secure Azure ML managed online endpoints with network isolation	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-online-endpoint?view=azureml-api-2
Secure Azure ML RAG workflows with network isolation	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-rag-workflows?view=azureml-api-2
Secure Azure ML training with virtual networks	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-training-vnet?view=azureml-api-2
Secure Azure ML workspace using virtual networks	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-secure-workspace-vnet?view=azureml-api-2
Configure RBAC access to Azure ML feature store	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-access-control-feature-store?view=azureml-api-2
Set up authentication to Azure ML workspaces	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-authentication?view=azureml-api-2
Configure customer-managed keys for Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-customer-managed-keys?view=azureml-api-2
Securely use Key Vault secrets in Azure ML runs	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-secrets-in-runs?view=azureml-api-2
Apply built-in Azure Policy definitions for AML	https://learn.microsoft.com/en-us/azure/machine-learning/policy-reference?view=azureml-api-2
Manage credentials with connections in Azure ML prompt flow	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-connections?view=azureml-api-2
Configure network isolation for Prompt flow in Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-secure-prompt-flow?view=azureml-api-2
Apply Azure Policy regulatory controls to Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/security-controls-policy?view=azureml-api-2
Create secure Azure ML workspace with managed VNet	https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-create-secure-workspace?view=azureml-api-2