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MicrosoftDocs/azure-machine-learning

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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, 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).

globalRequires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or fetch_webpage to retrieve documentation.
generated_at:2026-06-21
generator:docs2skills/1.0.0
New~21.6k
v1.0Saved Jun 26, 2026

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-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, quotas and service limits, supported VM SKUs, and how to plan deployment capacity within those constraints
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-L455 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 L456-L513 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 L514-L555 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-learning/reference-automl-images-cli-multilabel-classification?view=azureml-api-2
Author AutoML image object detection YAML jobs https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-cli-object-detection?view=azureml-api-2
Configure AutoML vision hyperparameters in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-hyperparameters?view=azureml-api-2
Format JSONL data for AutoML computer vision https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-images-schema?view=azureml-api-2
Configure AutoML multilabel text classification YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-nlp-cli-multilabel-classification?view=azureml-api-2
Author AutoML NLP NER jobs using YAML schema https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-nlp-cli-ner?view=azureml-api-2
Define AutoML text classification jobs with YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-automl-nlp-cli-text-classification?view=azureml-api-2
Reference configuration for Kubernetes with Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/reference-kubernetes?view=azureml-api-2
Define command components via Azure ML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-command?view=azureml-api-2
Author pipeline components using Azure ML YAML schema https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-pipeline?view=azureml-api-2
Configure Spark components in Azure ML YAML schema https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-component-spark?view=azureml-api-2
Configure AmlCompute clusters via YAML in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-compute-aml?view=azureml-api-2
Define Azure ML compute instances with YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-compute-instance?view=azureml-api-2
Configure attached Kubernetes clusters in Azure ML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-compute-kubernetes?view=azureml-api-2
Attach and configure VMs via Azure ML YAML schema https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-compute-vm?view=azureml-api-2
Configure AI Content Safety connections in AML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-ai-content-safety?view=azureml-api-2
Author AI Search connection YAML for AML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-ai-search?view=azureml-api-2
Configure Foundry Tools connections with Azure ML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-ai-services?view=azureml-api-2
Define API key connections via AML YAML schema https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-api-key?view=azureml-api-2
Define Azure OpenAI connections via AML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-azure-openai?view=azureml-api-2
Define blob datastore connections in AML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-blob?view=azureml-api-2
Configure Azure Container Registry connections in AML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-container-registry?view=azureml-api-2
Author custom key connections in Azure ML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-custom-key?view=azureml-api-2
Configure Data Lake Gen2 connections via AML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-data-lake?view=azureml-api-2
Configure Git repository connections in AML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-git?view=azureml-api-2
Set up OneLake connections using AML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-onelake?view=azureml-api-2
Configure OpenAI service connections in AML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-openai?view=azureml-api-2
Set up Python feed connections using AML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-python-feed?view=azureml-api-2
Define Serp connections via Azure ML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-serp?view=azureml-api-2
Author serverless connection YAML for Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-serverless?view=azureml-api-2
Configure AI Speech Services connections in AML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-connection-speech?view=azureml-api-2
Understand core Azure ML CLI v2 YAML syntax https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-core-syntax?view=azureml-api-2
Reference schema for Azure ML data YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-data?view=azureml-api-2
Define Azure Blob datastores via YAML in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-blob?view=azureml-api-2
Author Azure Data Lake Gen1 datastore YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-data-lake-gen1?view=azureml-api-2
Configure Azure Data Lake Gen2 datastores in YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-data-lake-gen2?view=azureml-api-2
Configure Azure Files datastores using YAML schema https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-datastore-files?view=azureml-api-2
Author batch deployment YAML for Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-batch?view=azureml-api-2
Define Kubernetes online deployments in Azure ML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-kubernetes-online?view=azureml-api-2
Configure managed online deployments via YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-managed-online?view=azureml-api-2
Configure Azure ML deployment template YAML schema https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-template?view=azureml-api-2
Author batch endpoint YAML for Azure ML CLI v2 https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-endpoint-batch?view=azureml-api-2
Configure Azure ML online endpoints with YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-endpoint-online?view=azureml-api-2
Reference schema for Azure ML environment YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-environment?view=azureml-api-2
Author feature entity definitions via Azure ML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-feature-entity?view=azureml-api-2
Create feature retrieval specs with Azure ML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-feature-retrieval-spec?view=azureml-api-2
Configure feature sets in Azure ML YAML schema https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-feature-set?view=azureml-api-2
Define feature stores in Azure ML using YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-feature-store?view=azureml-api-2
Define feature set specifications using YAML schema https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-featureset-spec?view=azureml-api-2
Configure Azure ML CLI v2 command job YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-command?view=azureml-api-2
Create parallel jobs in Azure ML pipeline YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-parallel?view=azureml-api-2
Author pipeline job definitions with AML YAML schema https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-pipeline?view=azureml-api-2
Configure Azure ML pipeline jobs using YAML schema https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-pipeline?view=azureml-api-2
Configure Spark jobs in Azure ML with YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-spark?view=azureml-api-2
Define sweep (hyperparameter) jobs with Azure ML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-job-sweep?view=azureml-api-2
Reference schema for Azure ML MLTable YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-mltable?view=azureml-api-2
Define Azure ML models with CLI v2 YAML schema https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-model?view=azureml-api-2
Create model monitoring schedules with Azure ML YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-monitor?view=azureml-api-2
Navigate Azure ML CLI v2 YAML schema references https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-overview?view=azureml-api-2
Define Azure ML registries using CLI v2 YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-registry?view=azureml-api-2
Author data import schedule YAML for Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-schedule-data-import?view=azureml-api-2
Configure Azure ML job schedules with YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-schedule?view=azureml-api-2
Reference schema for Azure ML workspace YAML https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-workspace?view=azureml-api-2

Integrations & Coding Patterns

Topic URL
Configure input data sources for AML batch endpoint jobs https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-data-batch-endpoints-jobs?view=azureml-api-2
Set up Azure Databricks with AutoML in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-databricks-automl-environment?view=azureml-api-1
Ingest data to Azure ML with Data Factory https://learn.microsoft.com/en-us/azure/machine-learning/how-to-data-ingest-adf?view=azureml-api-1
Wrangle data using Synapse Spark with Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-data-prep-synapse-spark-pool?view=azureml-api-1
Configure Azure ML datastores for storage access https://learn.microsoft.com/en-us/azure/machine-learning/how-to-datastore?view=azureml-api-2
Configure Azure ML datastores for storage access https://learn.microsoft.com/en-us/azure/machine-learning/how-to-datastore?view=azureml-api-2
Run MLflow models in Azure ML Spark jobs https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-model-spark-jobs?view=azureml-api-2
Deploy AML models as custom skills for Azure AI Search https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-model-cognitive-search?view=azureml-api-1
Deploy Hugging Face transformer models to Azure ML endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-models-from-huggingface?view=azureml-api-2
Use Azure ML REST API for online deployments https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-with-rest?view=azureml-api-2
Import data into Azure ML designer https://learn.microsoft.com/en-us/azure/machine-learning/how-to-designer-import-data?view=azureml-api-1
Run custom Python code in Azure ML designer pipelines https://learn.microsoft.com/en-us/azure/machine-learning/how-to-designer-python?view=azureml-api-1
Run local ONNX inference for Azure AutoML image models https://learn.microsoft.com/en-us/azure/machine-learning/how-to-inference-onnx-automl-image-models?view=azureml-api-2
Use Azure ML inference HTTP server for local debugging https://learn.microsoft.com/en-us/azure/machine-learning/how-to-inference-server-http?view=azureml-api-2
Log metrics and artifacts with MLflow in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics?view=azureml-api-2
Manage Azure ML resources using REST APIs https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-rest?view=azureml-api-2
Define and use MLTable data in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mltable?view=azureml-api-2
Securely integrate Azure Synapse with Azure ML via VNets https://learn.microsoft.com/en-us/azure/machine-learning/how-to-private-endpoint-integration-synapse?view=azureml-api-2
Read and write data in Azure ML jobs (v2 SDK) https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2?view=azureml-api-2
Read and write data in Azure ML jobs (v2 SDK) https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2?view=azureml-api-2
Generate Responsible AI dashboards with Azure ML SDK https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-insights-sdk-cli?view=azureml-api-2
Attach secured Azure Databricks to Azure ML via private endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-securely-attach-databricks?view=azureml-api-2
Submit standalone and pipeline Spark jobs in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-submit-spark-jobs?view=azureml-api-2
Log metrics in Azure ML designer pipelines https://learn.microsoft.com/en-us/azure/machine-learning/how-to-track-designer-experiments?view=azureml-api-1
Train PyTorch models using Azure ML SDK v2 https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch?view=azureml-api-2
Use Azure AutoML ONNX models with ML.NET in .NET apps https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-automl-onnx-model-dotnet?view=azureml-api-2
Invoke Azure ML batch endpoints from Azure Data Factory https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-azure-data-factory?view=azureml-api-2
Access Azure ML batch endpoints from Microsoft Fabric https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-fabric?view=azureml-api-2
Trigger Azure ML batch endpoints from Event Grid https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-event-grid-batch?view=azureml-api-2
Integrate Azure ML events with Azure Event Grid https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-event-grid?view=azureml-api-2
Use labeled datasets from Azure ML labeling https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-labeled-dataset?view=azureml-api-1
Integrate Azure Databricks MLflow tracking with Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-databricks?view=azureml-api-2
Configure MLflow tracking from Azure Synapse to Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-synapse?view=azureml-api-2
Integrate Azure Synapse Spark in Azure ML pipelines https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-synapsesparkstep?view=azureml-api-1
Create and use custom tool packages in prompt flow https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-custom-tool-package-creation-and-usage?view=azureml-api-2
Develop Prompt Flow and chat flows in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-develop-flow?view=azureml-api-2
Use streaming endpoints from Prompt Flow deployments https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-enable-streaming-mode?view=azureml-api-2
Evaluate Semantic Kernel plugins using Azure ML prompt flow https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-evaluate-semantic-kernel?view=azureml-api-2
Integrate LangChain workflows into Azure ML prompt flow https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-integrate-with-langchain?view=azureml-api-2
Rebuild Prompt Flow workflows using Microsoft Agent Framework https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-migrate-prompt-flow-to-agent-framework?view=azureml-api-2
Incorporate image inputs into Prompt Flow pipelines https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-process-image?view=azureml-api-2
Use Azure OpenAI GPT-4 Turbo with Vision tool in prompt flow https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/azure-open-ai-gpt-4v-tool?view=azureml-api-2
Use Content Safety text tool in prompt flow https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/content-safety-text-tool?view=azureml-api-2
Configure the embedding tool for prompt flow RAG https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/embedding-tool?view=azureml-api-2
Configure Index Lookup tool for vector search in prompt flow https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/index-lookup-tool?view=azureml-api-2
Configure and use the LLM tool in prompt flow https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/llm-tool?view=azureml-api-2
Configure Open Model LLM tool for open-source models https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/open-model-llm-tool?view=azureml-api-2
Integrate OpenAI GPT-4V vision model via prompt flow tool https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/openai-gpt-4v-tool?view=azureml-api-2
Use the prompt tool templates in prompt flow https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/prompt-tool?view=azureml-api-2
Build and configure Python tools in prompt flow https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/python-tool?view=azureml-api-2
Set up the rerank tool for prompt flow search https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/rerank-tool?view=azureml-api-2
Integrate SerpAPI search results via prompt flow tool https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/serp-api-tool?view=azureml-api-2
Quickstart: Configure Spark jobs in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/quickstart-spark-jobs?view=azureml-api-2
Map Azure ML v1 logging APIs to MLflow tracking https://learn.microsoft.com/en-us/azure/machine-learning/reference-migrate-sdk-v1-mlflow-tracking?view=azureml-api-2

Deployment

Topic URL
Consume Azure ML standard deployments across workspaces https://learn.microsoft.com/en-us/azure/machine-learning/how-to-connect-models-serverless?view=azureml-api-2
Convert ML notebooks to production scripts with MLOpsPython https://learn.microsoft.com/en-us/azure/machine-learning/how-to-convert-ml-experiment-to-production?view=azureml-api-1
Deploy AutoML models to AML online endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-automl-endpoint?view=azureml-api-2
Deploy AML models to Azure Container Instances with CLI v1 https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-container-instance?view=azureml-api-1
Deploy AML models to Azure Kubernetes Service with SDK/CLI v1 https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes-service?view=azureml-api-1
Deploy custom-container models to AML online endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-custom-container?view=azureml-api-2
Progressively deploy MLflow models to Azure ML online endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models-online-progressive?view=azureml-api-2
Deploy MLflow models to Azure ML endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models?view=azureml-api-2
Customize batch deployment outputs in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-model-custom-output?view=azureml-api-2
Deploy Azure ML registry models using deployment templates https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-models-deployment-template?view=azureml-api-2
Deploy catalog models as standard deployments in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-models-serverless?view=azureml-api-2
Deploy models to Azure ML managed online endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Deploy models to Azure ML managed online endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Deploy models to Azure ML managed online endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Deploy models to Azure ML managed online endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Deploy models to Azure ML managed online endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Publish and run Azure ML pipelines in production https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-pipelines?view=azureml-api-1
Deploy ONNX models with Triton on Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-with-triton?view=azureml-api-2
Create GitHub Actions CI/CD for Azure ML training https://learn.microsoft.com/en-us/azure/machine-learning/how-to-github-actions-machine-learning?view=azureml-api-2
Deploy image-processing models with AML batch endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-image-processing-batch?view=azureml-api-2
Deploy MLflow models to Azure ML batch endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mlflow-batch?view=azureml-api-2
Retrain Azure ML designer models via published pipelines https://learn.microsoft.com/en-us/azure/machine-learning/how-to-retrain-designer?view=azureml-api-1
Run Azure ML RAG prompt flows locally with VS Code https://learn.microsoft.com/en-us/azure/machine-learning/how-to-retrieval-augmented-generation-cloud-to-local?view=azureml-api-2
Deploy and trigger batch prediction pipelines in Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-run-batch-predictions-designer?view=azureml-api-1
Perform safe blue-green rollouts for Azure ML online endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-safely-rollout-online-endpoints?view=azureml-api-2
Set up end-to-end MLOps with Azure DevOps and Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-mlops-azureml?view=azureml-api-2
Set up end-to-end MLOps with GitHub and Azure ML https://learn.microsoft.com/en-us/azure/machine-learning/how-to-setup-mlops-github-azure-ml?view=azureml-api-2
Trigger published Azure ML pipelines automatically https://learn.microsoft.com/en-us/azure/machine-learning/how-to-trigger-published-pipeline?view=azureml-api-1
Deploy models for batch scoring with AML batch endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-model-deployments?view=azureml-api-2
Run Azure OpenAI embeddings via AML batch endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-model-openai-embeddings?view=azureml-api-2
Deploy and invoke pipelines via AML batch endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-pipeline-deployments?view=azureml-api-2
Convert existing AML pipeline jobs to batch endpoint deployments https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-pipeline-from-job?view=azureml-api-2
Operationalize scoring pipelines on AML batch endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-scoring-pipeline?view=azureml-api-2
Operationalize training pipelines on AML batch endpoints https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-batch-training-pipeline?view=azureml-api-2
Build RAG pipelines with Azure ML and prompt flow https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-pipelines-prompt-flow?view=azureml-api-2
Deploy migrated Agent Framework workflows to Azure https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-deploy-migrated-agent-framework-workflow?view=azureml-api-2
Deploy Prompt Flow to Azure ML online endpoints with CLI https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-deploy-to-code?view=azureml-api-2
Implement GenAIOps with prompt flow and Azure DevOps pipelines https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-end-to-end-azure-devops-with-prompt-flow?view=azureml-api-2
Integrate prompt flow with DevOps pipelines for LLM apps https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-integrate-with-llm-app-devops?view=azureml-api-2
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Overall Score

72/100

Grade

B

Good

Safety

80

Quality

68

Clarity

78

Completeness

65

Summary

This skill provides expert guidance for Azure Machine Learning development, covering troubleshooting, best practices, decision-making, architecture patterns, security, configuration, integrations, and deployment. It combines local quick-reference content with remote documentation fetching via Microsoft Learn, directing agents to read specific line ranges from the skill file or fetch remote documentation using mcp_microsoftdocs tools.

Detected Capabilities

fetch remote documentationnetwork access to learn.microsoft.commcp_microsoftdocs tool integrationfetch_webpage fallbackstructured reference guidancecross-reference URL lookups

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

troubleshoot azure ml pipelineconfigure azure ml endpointazure ml network isolationazure ml mlops setupazure ml security best practicesdeploy prompt flowazure ml cost optimizationazure ml model deployment

Risk Signals

INFO

Network access required for documentation fetching

Compatibility field and 'How to Use' section
INFO

External tool dependency (mcp_microsoftdocs or fetch_webpage)

Compatibility field
INFO

References 200+ external URLs from learn.microsoft.com

All category tables

Referenced Domains

External domains referenced in skill content, detected by static analysis.

github.comlearn.microsoft.com

Use Cases

  • Diagnose and troubleshoot Azure ML pipeline failures
  • Choose optimal training methods and compute targets for ML workloads
  • Architect secure Azure ML deployments with network isolation
  • Configure Azure ML components, datastores, and compute environments
  • Deploy ML models to online and batch endpoints
  • Integrate Azure ML with Synapse, Databricks, and Fabric
  • Set up MLOps workflows with GitHub Actions or Azure DevOps
  • Build and deploy Prompt Flow workflows and RAG applications

Quality Notes

  • Comprehensive category index with clear line ranges aids agent navigation
  • Well-organized into 8 logical domains (troubleshooting, architecture, security, etc.)
  • Detailed 200+ URL references provide exhaustive Azure ML documentation coverage
  • Clear instructions for agents on when to use local content vs. remote fetching
  • Metadata includes version freshness guidance (3-month staleness check)
  • Potential issue: Metadata generated_at is 2026-06-21 (future date, likely template error)
  • Skill assumes agent has access to mcp_microsoftdocs; gracefully suggests installation if unavailable
  • No edge case documentation for missing mcp_microsoftdocs or network failures beyond 'suggest install'
  • Line-range references (L35-L120) assume skill file itself contains supplementary content, but only SKILL.md is provided with no inline content at those ranges
Model: claude-haiku-4-5-20251001Analyzed: Jun 26, 2026

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