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azure-machine-learning

Expert knowledge for Azure Machine Learning development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integratio

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Imported from atc-net/atc-agentic-toolkit (plugins/azure/skills/azure-machine-learning/SKILL.md). Install upstream with npx skills add atc-net/atc-agentic-toolkit --skill azure-machine-learning. Copyright stays with the author.

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

This skill requires network access to fetch documentation content:

  • Preferred: Use mcp_microsoftdocs:microsoft_docs_fetch. Returns Markdown.
  • Fallback: Use WebFetch. Returns Markdown.

Category Index

Category Lines Description
Troubleshooting L33-L67 Diagnosing and fixing Azure ML errors: pipelines, AutoML, endpoints, networking, Kubernetes, environments, data access, prompt flow, and known issues/workspace diagnostics.
Best Practices L68-L89 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 L90-L120 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 L121-L127 Designing Azure ML inference architectures: choosing endpoint types, planning real-time online endpoints, and structuring data movement and multistep pipeline components.
Limits & Quotas L128-L136 Info on Azure ML regional/sovereign availability, quotas and service limits, supported VM SKUs, and how to plan deployment capacity within those constraints
Security L137-L190 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 L191-L451 How to configure Azure ML components, compute, networking, data, monitoring, AutoML, Prompt Flow, and all CLI/YAML schemas for jobs, deployments, feature stores, and connections.
Integrations & Coding Patterns L452-L509 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 L510-L551 Deploying and operationalizing models and pipelines on Azure ML (online/batch endpoints, AKS/ACI, registries), plus CI/CD, blue‑green rollouts, and MLOps/GenAIOps with GitHub/DevOps and prompt flow.

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.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 Azure ML prompt flow issues https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/troubleshoot-guidance?view=azureml-api-2
Troubleshoot Azure ML prompt flow issues 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
Ethical best practices for sourcing human data https://learn.microsoft.com/en-us/azure/machine-learning/concept-sourcing-human-data?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
Write advanced Azure ML entry scripts for inference https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-advanced-entry-script?view=azureml-api-1
Profile AML model CPU and memory usage before deployment https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-profile-model?view=azureml-api-1
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
Manage Azure ML compute notebook and terminal sessions https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-compute-sessions?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
Choose storage locations for Azure ML experiment files https://learn.microsoft.com/en-us/azure/machine-learning/how-to-save-write-experiment-files?view=azureml-api-1
Apply best practices for distributed GPU training 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
Tune prompts using variants in Prompt Flow https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-tune-prompts-using-variants?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 Azure ML designer algorithms with cheat sheet https://learn.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet?view=azureml-api-1
Plan Azure ML registries for multi-environment MLOps https://learn.microsoft.com/en-us/azure/machine-learning/concept-machine-learning-registries-mlops?view=azureml-api-2
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 the right Azure ML training method https://learn.microsoft.com/en-us/azure/machine-learning/concept-train-machine-learning-model?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 when and how to upgrade AML 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
Select appropriate Azure ML algorithms for tasks https://learn.microsoft.com/en-us/azure/machine-learning/how-to-select-algorithms?view=azureml-api-1
Use low-priority VMs for AML batch inference cost savings https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-low-priority-batch?view=azureml-api-2
Map AML 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 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
Evaluate compute management changes from AML 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
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
Plan around Azure ML prompt flow retirement and usage https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/overview-what-is-prompt-flow?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
Understand Azure ML endpoint types for inference https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints?view=azureml-api-2
Design data movement patterns in Azure ML pipelines https://learn.microsoft.com/en-us/azure/machine-learning/how-to-move-data-in-out-of-pipelines?view=azureml-api-1

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
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 in Azure ML VNets 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 for Azure ML images and components https://learn.microsoft.com/en-us/azure/machine-learning/concept-vulnerability-management?view=azureml-api-2
Configure Azure ML managed network to reach 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
Configure data authentication for Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-administrate-data-authentication?view=azureml-api-2
Manage Azure RBAC roles for Azure ML workspaces 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
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
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 account access keys https://learn.microsoft.com/en-us/azure/machine-learning/how-to-change-storage-access-key?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
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 virtual networks 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 resources https://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-customer-managed-keys?view=azureml-api-2
Securely use private Python packages in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-private-python-packages?view=azureml-api-1
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
Secure Prompt Flow with virtual network isolation 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
Secure Azure ML workspace with custom VNet https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-create-secure-workspace-vnet?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

Configuration

Topic URL
Configure AutoML Classification component with ML Tables https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/classification?view=azureml-api-2
Configure AutoML Forecasting component in designer https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/forecasting?view=azureml-api-2
Configure AutoML Image Multi-label Classification https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/image-classification-multilabel?view=azureml-api-2
Configure AutoML Image Classification component https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/image-classification?view=azureml-api-2
Configure AutoML Image Instance Segmentation component https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/image-instance-segmentation?view=azureml-api-2
Configure AutoML Image Object Detection component https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/image-object-detection?view=azureml-api-2
Configure AutoML Regression component with ML Tables https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/regression?view=azureml-api-2
Configure AutoML Text Multi-label Classification component https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/text-classification-multilabel?view=azureml-api-2
Configure AutoML Text Classification component https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/text-classification?view=azureml-api-2
Configure AutoML Text NER component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/text-ner?view=azureml-api-2
Configure Add Columns component to concatenate datasets https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/add-columns?view=azureml-api-2
Configure Add Rows component to append dataset records https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/add-rows?view=azureml-api-2
Configure Apply Image Transformation in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/apply-image-transformation?view=azureml-api-2
Configure Apply Math Operation component for column calculations https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/apply-math-operation?view=azureml-api-2
Configure Apply SQL Transformation component using SQLite https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/apply-sql-transformation?view=azureml-api-2
Configure Apply Transformation component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/apply-transformation?view=azureml-api-2
Configure Assign Data to Clusters in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/assign-data-to-clusters?view=azureml-api-2
Configure Boosted Decision Tree Regression component (LightGBM) https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/boosted-decision-tree-regression?view=azureml-api-2
Configure Clean Missing Data component for handling nulls https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/clean-missing-data?view=azureml-api-2
Configure Clip Values component to handle outliers https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/clip-values?view=azureml-api-2
Configure and use Azure ML designer algorithm components https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/component-reference?view=azureml-api-2
Configure Convert to CSV component for dataset export https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-to-csv?view=azureml-api-2
Configure Convert to Dataset component for internal format https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-to-dataset?view=azureml-api-2
Configure Convert to Image Directory in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-to-image-directory?view=azureml-api-2
Configure Convert to Indicator Values for categorical encoding https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-to-indicator-values?view=azureml-api-2
Configure Convert Word to Vector component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/convert-word-to-vector?view=azureml-api-2
Configure Create Python Model component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/create-python-model?view=azureml-api-2
Configure Cross Validate Model component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/cross-validate-model?view=azureml-api-2
Configure Decision Forest Regression in Azure ML designer https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/decision-forest-regression?view=azureml-api-2
Configure DenseNet image classification component https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/densenet?view=azureml-api-2
Configure Edit Metadata component to adjust column roles https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/edit-metadata?view=azureml-api-2
Set up Enter Data Manually component for small datasets https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/enter-data-manually?view=azureml-api-2
Configure Evaluate Model component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/evaluate-model?view=azureml-api-2
Configure Evaluate Recommender component for model accuracy https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/evaluate-recommender?view=azureml-api-2
Configure Execute Python Script in Azure ML designer https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/execute-python-script?view=azureml-api-2
Configure Execute R Script component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/execute-r-script?view=azureml-api-2
Configure Export Data component to save pipeline outputs https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/export-data?view=azureml-api-2
Configure Extract N-Gram Features from Text in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/extract-n-gram-features-from-text?view=azureml-api-2
Configure Fast Forest Quantile Regression in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/fast-forest-quantile-regression?view=azureml-api-2
Configure Feature Hashing text component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/feature-hashing?view=azureml-api-2
Configure Filter Based Feature Selection for predictive columns https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/filter-based-feature-selection?view=azureml-api-2
Use graph search query syntax in Azure ML designer https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/graph-search-syntax?view=azureml-api-2
Configure Group Data into Bins component for discretization https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/group-data-into-bins?view=azureml-api-2
Configure Import Data component for Azure ML designer pipelines https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/import-data?view=azureml-api-2
Configure Init Image Transformation in Azure ML designer https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/init-image-transformation?view=azureml-api-2
Configure Join Data component to merge datasets https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/join-data?view=azureml-api-2
Configure K-Means Clustering component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/k-means-clustering?view=azureml-api-2
Configure Latent Dirichlet Allocation component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/latent-dirichlet-allocation?view=azureml-api-2
Configure Linear Regression component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/linear-regression?view=azureml-api-2
Configure Multiclass Boosted Decision Tree in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/multiclass-boosted-decision-tree?view=azureml-api-2
Configure Multiclass Decision Forest in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/multiclass-decision-forest?view=azureml-api-2
Configure Multiclass Logistic Regression in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/multiclass-logistic-regression?view=azureml-api-2
Configure Multiclass Neural Network in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/multiclass-neural-network?view=azureml-api-2
Set up Neural Network Regression in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/neural-network-regression?view=azureml-api-2
Configure Normalize Data component for feature scaling https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/normalize-data?view=azureml-api-2
Configure One-vs-All Multiclass component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/one-vs-all-multiclass?view=azureml-api-2
Configure One-vs-One Multiclass component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/one-vs-one-multiclass?view=azureml-api-2
Configure Partition and Sample component for dataset splitting https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/partition-and-sample?view=azureml-api-2
Configure deprecated PCA-Based Anomaly Detection component https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/pca-based-anomaly-detection?view=azureml-api-2
Configure Permutation Feature Importance component for model insights https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/permutation-feature-importance?view=azureml-api-2
Use Poisson Regression component in Azure ML designer https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/poisson-regression?view=azureml-api-2
Configure Preprocess Text component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/preprocess-text?view=azureml-api-2
Configure Remove Duplicate Rows component for deduplication https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/remove-duplicate-rows?view=azureml-api-2
Configure ResNet image classification in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/resnet?view=azureml-api-2
Configure Score Image Model component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-image-model?view=azureml-api-2
Configure Score Model component in Azure ML designer https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-model?view=azureml-api-2
Configure Score SVD Recommender for predictions https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-svd-recommender?view=azureml-api-2
Configure Score Vowpal Wabbit Model in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-vowpal-wabbit-model?view=azureml-api-2
Configure Score Wide & Deep Recommender component https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/score-wide-and-deep-recommender?view=azureml-api-2
Configure Select Columns in Dataset to subset features https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/select-columns-in-dataset?view=azureml-api-2
Configure Select Columns Transform for stable feature sets https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/select-columns-transform?view=azureml-api-2
Configure SMOTE component to oversample minority classes https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/smote?view=azureml-api-2
Configure Split Data component for train-test partitioning https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/split-data?view=azureml-api-2
Configure Split Image Directory component for datasets https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/split-image-directory?view=azureml-api-2
Configure Summarize Data component for descriptive statistics https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/summarize-data?view=azureml-api-2
Configure Train Anomaly Detection Model component https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-anomaly-detection-model?view=azureml-api-2
Configure Train Clustering Model component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-clustering-model?view=azureml-api-2
Configure Train PyTorch Model component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-pytorch-model?view=azureml-api-2
Configure Train SVD Recommender in Azure ML designer https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-svd-recommender?view=azureml-api-2
Configure Train Vowpal Wabbit Model in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-vowpal-wabbit-model?view=azureml-api-2
Configure Train Wide & Deep Recommender component https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/train-wide-and-deep-recommender?view=azureml-api-2
Configure Tune Model Hyperparameters in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/tune-model-hyperparameters?view=azureml-api-2
Configure Two-Class Averaged Perceptron in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-averaged-perceptron?view=azureml-api-2
Configure Two-Class Boosted Decision Tree in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-boosted-decision-tree?view=azureml-api-2
Configure Two-Class Decision Forest in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-decision-forest?view=azureml-api-2
Configure Two-Class Logistic Regression in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-logistic-regression?view=azureml-api-2
Configure Two-Class Neural Network in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-neural-network?view=azureml-api-2
Configure Two-Class SVM component in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-support-vector-machine?view=azureml-api-2
Configure Web Service Input and Output components https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/web-service-input-output?view=azureml-api-2
Configure inference data collection for Azure ML endpoints https://learn.microsoft.com/en-us/azure/machine-learning/concept-data-collection?view=azureml-api-2
Use expressions in Azure ML SDK and CLI v2 jobs https://learn.microsoft.com/en-us/azure/machine-learning/concept-expressions?view=azureml-api-2
Specify models for Azure ML online deployments https://learn.microsoft.com/en-us/azure/machine-learning/concept-online-deployment-model-specification?view=azureml-api-2
Use Azure ML prebuilt Docker images for inference https://learn.microsoft.com/en-us/azure/machine-learning/concept-prebuilt-docker-images-inference?view=azureml-api-2
Configure and use Azure ML Responsible AI dashboard https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai-dashboard?view=azureml-api-2
Use workspace soft delete and recovery in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/concept-soft-delete?view=azureml-api-2
Configure and use vector stores in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/concept-vector-stores?view=azureml-api-2
Link OneLake tables to Azure ML via UI datastores https://learn.microsoft.com/en-us/azure/machine-learning/create-datastore-with-user-interface?view=azureml-api-2
Configure feature retrieval specs for training and inference https://learn.microsoft.com/en-us/azure/machine-learning/feature-retrieval-concepts?view=azureml-api-2
Configure feature set materialization in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/feature-set-materialization-concepts?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
Access Azure cloud storage data during interactive ML development https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-data-interactive?view=azureml-api-2
Configure Kubernetes compute targets for Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-anywhere?view=azureml-api-2
Configure Kubernetes compute targets for Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-anywhere?view=azureml-api-2
Configure Kubernetes compute targets for Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-anywhere?view=azureml-api-2
Attach AKS or Arc Kubernetes clusters to Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-attach-kubernetes-to-workspace?view=azureml-api-2
Configure Azure AutoML for time-series forecasting https://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-forecast?view=azureml-api-2
Configure AutoML computer vision training in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models?view=azureml-api-2
Configure Azure AutoML for custom NLP training https://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-nlp-models?view=azureml-api-2
Configure autoscaling for Azure ML online endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-autoscale-endpoints?view=azureml-api-2
Configure custom Azure Container for PyTorch environments https://learn.microsoft.com/en-us/azure/machine-learning/how-to-azure-container-for-pytorch-environment?view=azureml-api-2
Enable production inference data collection for Azure ML endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-collect-production-data?view=azureml-api-2
Customize AutoML data featurization settings in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-features?view=azureml-api-1
Configure Azure AutoML tabular training with SDK v2 https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-train?view=azureml-api-2
Configure data splits and cross-validation in Azure AutoML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits?view=azureml-api-1
Maintain network isolation with Azure ML v2 API https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-network-isolation-with-v2?view=azureml-api-2
Configure private endpoints for Azure ML workspaces https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-private-link?view=azureml-api-2
Configure Azure storage connections in ML Studio (v1) https://learn.microsoft.com/en-us/azure/machine-learning/how-to-connect-data-ui?view=azureml-api-1
Configure Azure ML connections to external data and services https://learn.microsoft.com/en-us/azure/machine-learning/how-to-connection?view=azureml-api-2
Create and manage Azure ML compute clusters https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-cluster?view=azureml-api-2
Configure and manage Azure ML compute in studio https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio?view=azureml-api-2
Create Azure ML compute instances for development https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-compute-instance?view=azureml-api-2
Create Azure ML compute instances for development https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-compute-instance?view=azureml-api-2
Create and manage Azure ML data assets https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets?view=azureml-api-2
Create and manage Azure ML data assets https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets?view=azureml-api-2
Configure image labeling projects in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-image-labeling-projects?view=azureml-api-2
Configure text labeling projects in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-text-labeling-projects?view=azureml-api-2
Configure and use vector indexes in Azure ML prompt flow https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-vector-index?view=azureml-api-2
Create Azure ML workspaces with ARM templates https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-workspace-template?view=azureml-api-2
Set up custom DNS for private Azure ML workspaces https://learn.microsoft.com/en-us/azure/machine-learning/how-to-custom-dns?view=azureml-api-2
Customize Azure ML compute instances with startup scripts https://learn.microsoft.com/en-us/azure/machine-learning/how-to-customize-compute-instance?view=azureml-api-2
Configure and use Azure ML datastores for storage access https://learn.microsoft.com/en-us/azure/machine-learning/how-to-datastore?view=azureml-api-2
Deploy Azure ML extension on Kubernetes clusters https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-kubernetes-extension?view=azureml-api-2
Enable Azure ML studio access within a virtual network https://learn.microsoft.com/en-us/azure/machine-learning/how-to-enable-studio-virtual-network?view=azureml-api-2
Export or delete Azure ML workspace data https://learn.microsoft.com/en-us/azure/machine-learning/how-to-export-delete-data?view=azureml-api-2
Customize Azure ML prebuilt Docker images for inference https://learn.microsoft.com/en-us/azure/machine-learning/how-to-extend-prebuilt-docker-image-inference?view=azureml-api-1
Import external data into Azure ML (preview) https://learn.microsoft.com/en-us/azure/machine-learning/how-to-import-data-assets?view=azureml-api-2
Label images and text in Azure ML projects https://learn.microsoft.com/en-us/azure/machine-learning/how-to-label-data?view=azureml-api-2
Link Synapse and Azure ML workspaces with Spark pools https://learn.microsoft.com/en-us/azure/machine-learning/how-to-link-synapse-ml-workspaces?view=azureml-api-1
Log MLflow models as first-class models in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-mlflow-models?view=azureml-api-2
Send Azure ML distributed training logs to Application Insights https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-search?view=azureml-api-2
Configure model interpretability in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability?view=azureml-api-2
Manage Azure ML compute instances and lifecycle https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-compute-instance?view=azureml-api-2
Configure Azure ML environments with CLI and SDK https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2?view=azureml-api-2
Configure Azure ML environments with CLI and SDK https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2?view=azureml-api-2
Create Azure ML hub workspaces with Bicep templates https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-hub-workspace-template?view=azureml-api-2
Manage component and pipeline inputs/outputs in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-inputs-outputs-pipeline?view=azureml-api-2
Create and manage Azure ML Kubernetes instance types https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-kubernetes-instance-types?view=azureml-api-2
Administer and export Azure ML labeling projects https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-labeling-projects?view=azureml-api-2
Configure and publish Azure ML deployment templates https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-models-deployment-templates?view=azureml-api-2
Manage Azure ML model registry using MLflow https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-models-mlflow?view=azureml-api-2
Register and manage models with Azure ML CLI and SDK https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-models?view=azureml-api-2
Create and manage Azure ML registries https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-registries?view=azureml-api-2
Manage Azure ML resources using VS Code extension https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-resources-vscode?view=azureml-api-2
Attach and manage Synapse Spark pools in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-synapse-spark-pool?view=azureml-api-2
Provision Azure ML workspaces using Terraform https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace-terraform?view=azureml-api-2
Configure data drift monitors in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-datasets?view=azureml-api-1
Collect and monitor Kubernetes endpoint inference logs https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-kubernetes-online-enpoint-inference-server-log?view=azureml-api-2
Configure Azure ML model performance monitoring in production https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-model-performance?view=azureml-api-2
Configure monitoring and logging for Azure ML online endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-online-endpoints?view=azureml-api-2
Configure monitoring and logging for Azure ML online endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-monitor-online-endpoints?view=azureml-api-2
Extend Azure ML prebuilt inference images with Python https://learn.microsoft.com/en-us/azure/machine-learning/how-to-prebuilt-docker-images-inference-python-extensibility?view=azureml-api-1
Use R and RStudio on Azure ML compute instances https://learn.microsoft.com/en-us/azure/machine-learning/how-to-r-interactive-development?view=azureml-api-2
Configure network isolation for Azure ML registries https://learn.microsoft.com/en-us/azure/machine-learning/how-to-registry-network-isolation?view=azureml-api-2
Use Responsible AI dashboard tools in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard?view=azureml-api-2
Generate Responsible AI insights in Azure ML studio https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-insights-ui?view=azureml-api-2
Configure and export Responsible AI scorecards in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-scorecard?view=azureml-api-2
Schedule recurring data imports in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-schedule-data-import?view=azureml-api-2
Share models and components across Azure ML workspaces https://learn.microsoft.com/en-us/azure/machine-learning/how-to-share-models-pipelines-across-workspaces-with-registries?view=azureml-api-2
Query and compare MLflow experiments and runs in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments-mlflow?view=azureml-api-2
Submit MLflow Projects training jobs to Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-mlflow-projects?view=azureml-api-2
Configure and submit Azure ML training jobs https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-model?view=azureml-api-2
Configure and submit Azure ML training jobs https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-model?view=azureml-api-2
Train Azure ML models using custom Docker images (v1) https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-with-custom-image?view=azureml-api-1
Configure hyperparameter sweep jobs in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?view=azureml-api-2
Configure AutoMLStep in Azure ML pipelines https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-automlstep-in-pipelines?view=azureml-api-1
Use MLflow to track Azure ML experiments and runs https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-cli-runs?view=azureml-api-2
Configure MLflow tracking with Azure Machine Learning workspaces https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-configure-tracking?view=azureml-api-2
Configure and run parallel jobs in Azure ML pipelines https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-job-in-pipeline?view=azureml-api-2
Configure pipeline parameters in Azure ML designer https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-pipeline-parameter?view=azureml-api-1
Run training jobs on Azure ML serverless compute https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-serverless-compute?view=azureml-api-2
Configure hyperparameter sweep in Azure ML pipelines https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-sweep-in-pipeline?view=azureml-api-2
Configure dataset versioning in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-version-track-datasets?view=azureml-api-1
View and tag costs for Azure ML managed online endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-view-online-endpoints-costs?view=azureml-api-2
Configure VS Code remote sessions to Azure ML compute https://learn.microsoft.com/en-us/azure/machine-learning/how-to-work-in-vs-code-remote?view=azureml-api-2
Configure serverless Spark compute for Azure ML notebooks https://learn.microsoft.com/en-us/azure/machine-learning/interactive-data-wrangling-with-apache-spark-azure-ml?view=azureml-api-2
Reference Azure Machine Learning monitoring metrics and logs https://learn.microsoft.com/en-us/azure/machine-learning/monitor-azure-machine-learning-reference?view=azureml-api-2
Run batch evaluations for Prompt Flow at scale https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-bulk-test-evaluate-flow?view=azureml-api-2
Customize compute session base images for Azure ML prompt flow https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-customize-session-base-image?view=azureml-api-2
Create and customize evaluation flows and metrics https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-develop-an-evaluation-flow?view=azureml-api-2
Enable tracing and feedback for Prompt Flow deployments https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-enable-trace-feedback-for-deployment?view=azureml-api-2
Configure and manage Prompt Flow compute sessions https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-manage-compute-session?view=azureml-api-2
Configure Automated ML forecasting jobs via YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-automated-ml-forecasting?view=azureml-api-2
Author AutoML image classification jobs in YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-cli-classification?view=azureml-api-2
Define AutoML image instance segmentation YAML jobs https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-cli-instance-segmentation?view=azureml-api-2
Configure AutoML image multilabel classification YAML https://learn.microsoft.com/en-us/azure/machine-

Truncated - read the full file at https://github.com/atc-net/atc-agentic-toolkit/blob/58021a4e92a6bcd9e4b87a1cb77082ab1082e531/plugins/azure/skills/azure-machine-learning/SKILL.md.

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml
targets:
  - https://api.opensmartroute.ai/api/v1/registry/atc-net-atc-agentic-toolkit-azure-machine-learning/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

atc-net-atc-agentic-toolkit-azure-machine-learning.ocm.jsonjson
{
  "ocm": "1",
  "id": "atc-net-atc-agentic-toolkit-azure-machine-learning",
  "kind": "skill",
  "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 workspaces, compute clusters, pipelines, AutoML, online/batch endpoints, or Prompt Flow, and other Azure Machine Learning related development tasks. Not for Azure Databricks (use azure-databricks), Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Data Science Virtual Machines (use azure-data-science-vm).",
  "publisher": "atc-net",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "github"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "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 workspaces, compute clusters, pipelines, AutoML, online/batch endpoints, or Prompt Flow, and other Azure Machine Learning related development tasks. Not for Azure Databricks (use azure-databricks), Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Data Science Virtual Machines (use azure-data-science-vm)."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "github",
      "repository": "https://github.com/atc-net/atc-agentic-toolkit",
      "path": "plugins/azure/skills/azure-machine-learning/SKILL.md",
      "ref": "58021a4e92a6bcd9e4b87a1cb77082ab1082e531",
      "url": "https://github.com/atc-net/atc-agentic-toolkit/blob/58021a4e92a6bcd9e4b87a1cb77082ab1082e531/plugins/azure/skills/azure-machine-learning/SKILL.md",
      "key": "atc-net/atc-agentic-toolkit/plugins/azure/skills/azure-machine-learning/SKILL.md"
    },
    "compatibility": "Requires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or WebFetch to retrieve documentation."
  },
  "instructions": "# Azure Machine Learning Skill\n\nThis 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.\n\n## How to Use This Skill\n\n> **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",
  "cost": {
    "context_tokens": 21385
  }
}

Fetch it by URL: GET /api/v1/registry/atc-net-atc-agentic-toolkit-azure-machine-learning/manifest?version=1.0.0

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