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 runtime issues: pipelines, AutoML, endpoints (online/batch), Kubernetes, networking (VNet/private), environments/images, prompt flow, and known platform issues.
Best Practices	L72-L95	Best practices for ML/LLM lifecycle in Azure ML: cost, security, data ethics, feature design, training, deployment, monitoring, AutoML, prompt flow, and performance tuning.
Decision Making	L96-L119	Guidance on Azure ML design choices: algorithms, training, networking, cost, DR, data labeling, and detailed migration/upgrade paths from AML v1 to v2 across jobs, data, compute, and workspaces
Architecture & Design Patterns	L120-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	Azure ML deployment limits: regional/sovereign availability, quota management, supported VM SKUs for managed endpoints, and capacity planning against service limits.
Security	L137-L195	Securing Azure ML workspaces, data, and endpoints: encryption, auth/RBAC, managed identities, network isolation/VNets, private endpoints, policies, key/secret management, and compliance controls.
Configuration	L196-L463	Configuring Azure ML: designer components, AutoML, compute, data, monitoring, registries, prompt flow, and full CLI/YAML schemas for jobs, endpoints, feature stores, and connections.
Integrations & Coding Patterns	L464-L506	Integrating Azure ML with data sources, Spark/Databricks/Synapse, REST/MLflow, prompt flow, and external services to move data, run jobs, deploy models, and trigger/monitor endpoints.
Deployment	L507-L553	Deploying and operating ML and LLM workloads in Azure ML: online/batch endpoints, MLflow, pipelines, prompt flow, CI/CD, blue‑green rollouts, and cross-workspace/model catalog deployments

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
Debug pipeline reuse behavior in Azure Machine Learning	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 execution	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
Troubleshoot 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 issue analysis	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
Apply secure coding practices in Azure ML notebooks	https://learn.microsoft.com/en-us/azure/machine-learning/concept-secure-code-best-practice?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
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
Apply generative AI monitoring metrics and recommended practices in Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-model-monitoring-generative-ai-evaluation-metrics?view=azureml-api-2
Design and use evaluation flows and metrics in prompt flow	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-develop-an-evaluation-flow?view=azureml-api-2
Tune LLM prompts using variants in Azure ML 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
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
Use vendor companies for Azure ML data labeling	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-outsource-data-labeling?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
Upgrade model management workflows from AML v1 to v2	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-assets-model?view=azureml-api-2
Migrate script run jobs to AML SDK v2 command jobs	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-command-job?view=azureml-api-2
Upgrade AutoML configurations from AML SDK v1 to v2	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-execution-automl?view=azureml-api-2
Compare local run workflows between AML v1 and v2	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-local-runs?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
Compare workspace management between AML SDK v1 and v2	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-resource-workspace?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
Design multistep pipeline components in Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-pipeline-component?view=azureml-api-2

Limits & Quotas

Topic	URL
Check regional availability for Azure ML standard 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
Use customer-managed keys with Azure Machine Learning	https://learn.microsoft.com/en-us/azure/machine-learning/concept-customer-managed-keys?view=azureml-api-2
Implement data encryption for Azure ML storage and compute	https://learn.microsoft.com/en-us/azure/machine-learning/concept-data-encryption?view=azureml-api-2
Understand data handling and privacy for Model Catalog deployments	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 inbound and outbound network traffic for Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-azureml-behind-firewall?view=azureml-api-2
Securely access on-premises resources from Azure ML	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
Authenticate and authorize access to AML batch endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-batch-endpoint?view=azureml-api-2
Authenticate clients to 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
Maintain network isolation with Azure ML v2 ARM APIs	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
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
Enable Azure ML studio access inside virtual networks	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-enable-studio-virtual-network?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 Azure ML managed virtual network isolation	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
Isolate Azure ML registries with VNets and private endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-registry-network-isolation?view=azureml-api-2
Configure network isolation for AML batch endpoints	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 environments with VNets	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 API and data source credentials with prompt flow connections	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-connections?view=azureml-api-2
Secure prompt flow with virtual network isolation in Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-secure-prompt-flow?view=azureml-api-2
Apply Azure Policy regulatory controls to Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/security-controls-policy?view=azureml-api-2
Create secure Azure ML workspace in a VNet	https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-create-secure-workspace-vnet?view=azureml-api-2
Create a 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
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 Git integration for Azure ML training jobs	https://learn.microsoft.com/en-us/azure/machine-learning/concept-train-model-git-integration?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 datastore UI	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
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 Kubernetes clusters to Azure ML workspaces	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
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
Create and configure 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
Configure custom DNS for Azure ML private endpoints	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
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 as data assets	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 via CLI and SDK	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-environments-v2?view=azureml-api-2
Manage Azure ML environments via 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 lifecycle and auto-delete for imported data assets	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-imported-data-assets?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
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 the 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 Machine Learning compute	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-r-interactive-development?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
Configure Azure ML training jobs and compute targets (v1)	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets?view=azureml-api-1
Share data assets across Azure ML workspaces via registries	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-share-data-across-workspaces-with-registries?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 (v2)	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-model?view=azureml-api-2
Configure and submit Azure ML training jobs (v2)	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 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
Configure custom base images for prompt flow sessions	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-customize-session-base-image?view=azureml-api-2
Configure and consume streaming responses from prompt flow endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-enable-streaming-mode?view=azureml-api-2
Enable tracing and user feedback collection 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 in Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-manage-compute-session?view=azureml-api-2
Configure monitoring for Azure ML generative AI apps	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-monitor-generative-ai-applications?view=azureml-api-2
Configure Azure OpenAI GPT-4 Turbo with Vision tool	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/azure-open-ai-gpt-4v-tool?view=azureml-api-2
Configure 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 embedding tool for OpenAI in prompt flow	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/embedding-tool?view=azureml-api-2
Configure Index Lookup tool for RAG in prompt flow	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/index-lookup-tool?view=azureml-api-2
Configure LLM tool in Azure ML prompt flow	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/llm-tool?view=azureml-api-2
Use Open Model LLM tool in Azure ML prompt flow	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/open-model-llm-tool?view=azureml-api-2
Configure OpenAI GPT-4V tool in Azure ML prompt flow	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/openai-gpt-4v-tool?view=azureml-api-2
Configure and manage tools in Azure ML prompt flow	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/overview?view=azureml-api-2
Use and configure prompt templates in prompt flow	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/prompt-tool?view=azureml-api-2
Create 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
Configure Rerank tool for RAG in prompt flow	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/rerank-tool?view=azureml-api-2
Configure SerpAPI search tool in Azure ML prompt flow	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/tools-reference/serp-api-tool?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
Author deployment template YAML for Azure ML CLI v2	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
Author command job YAML for Azure ML CLI v2	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 using 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
Connect storage to Azure ML via studio UI	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-connect-data-ui?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
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	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	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
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 AML batch endpoints from Event Grid and storage events	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 Azure ML 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
Evaluate Semantic Kernel plugins and planners with 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 with Azure ML prompt flow	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-integrate-with-langchain?view=azureml-api-2
Incorporate image inputs into Azure ML prompt flows	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-process-image?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
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
Progressively roll out MLflow models on 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 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 machine learning models to Azure ML online endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Deploy models to AML managed online endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Deploy models to AML managed online endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Deploy models to AML managed online endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Deploy models to AML managed online endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Deploy models to AML managed online endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-online-endpoints?view=azureml-api-2
Deploy Azure ML pipelines as batch endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-pipeline-component-as-batch-endpoint?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
Serve models with NVIDIA Triton on AML endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-with-triton?view=azureml-api-2
Build Azure ML CI/CD pipelines with Azure DevOps	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-devops-machine-learning?view=azureml-api-2
Create GitHub Actions workflows for Azure ML CI/CD	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 for batch inference with Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mlflow-batch?view=azureml-api-2
Run language models with AML batch endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-nlp-processing-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 prompt flows as managed online endpoints for real-time inference	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-deploy-for-real-time-inference?view=azureml-api-2
Deploy prompt flows to managed or Kubernetes 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
Implement GenAIOps with prompt flow and GitHub pipelines	https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-end-to-end-llmops-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