azure-machine-learning

name	azure-machine-learning
description	Expert knowledge for Azure Machine Learning development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using AutoML, managed online endpoints, Prompt Flow/RAG, feature store, or MLflow integrations, and other Azure Machine Learning related development tasks. Not for Azure Databricks (use azure-databricks), Azure Synapse Analytics (use azure-synapse-analytics), Azure Data Science Virtual Machines (use azure-data-science-vm), Azure HDInsight (use azure-hdinsight).
compatibility	Requires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or fetch_webpage to retrieve documentation.
metadata	{"generated_at":"2026-08-02","generator":"docs2skills/1.0.0"}

Azure Machine Learning Skill

This skill provides expert guidance for Azure Machine Learning. Covers troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.

How to Use This Skill

IMPORTANT for Agent: Use the Category Index below to locate relevant sections. For categories with line ranges (e.g., L35-L120), use read_file with the specified lines. For categories with file links (e.g., [security.md](security.md)), use read_file on the linked reference file

IMPORTANT for Agent: If metadata.generated_at is more than 3 months old, suggest the user pull the latest version from the repository. If mcp_microsoftdocs tools are not available, suggest the user install it: Installation Guide

This skill requires network access to fetch documentation content:

• Preferred: Use mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.
• Fallback: Use fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.

Category Index

Category	Lines	Description
Troubleshooting	L37-L65	Diagnosing and fixing Azure ML issues: pipelines, endpoints, networking, Kubernetes, environments, AutoML, prompt flow, feature store, and known platform bugs/errors.
Best Practices	L66-L82	Guidance on ML best practices: cost and compute optimization, AutoML tuning, model monitoring, feature engineering, batch scoring, GPU/distributed training, and inference performance.
Decision Making	L83-L109	Guides for planning and decision-making in Azure ML: choosing training/network options, DR/failover, and detailed migration/upgrade paths from v1 to v2, ACI, Prompt Flow, and data/compute assets.
Architecture & Design Patterns	L110-L115	Designing Azure ML inference architectures: choosing endpoint types, planning real-time online endpoints, and structuring data movement and multistep pipeline components.
Limits & Quotas	L116-L125	Limits, quotas, and availability for Azure ML: regional/sovereign support, VM SKUs, workspace soft delete, and capacity planning for managed online endpoints.
Security	L126-L175	Securing Azure ML: encryption, keys, identity/RBAC, policies, network isolation/VNets, private endpoints, DNS, data exfil prevention, and secure access to endpoints, storage, Key Vault, and prompt flows.
Configuration	L176-L412	Configuring Azure ML components, jobs, and infrastructure: AutoML, designer components, YAML schemas, compute, networking, data, monitoring, Responsible AI, and prompt flow setups.
Integrations & Coding Patterns	L413-L457	Patterns and code for integrating Azure ML with data sources, Spark, MLflow, REST/HTTP, Synapse/Databricks/Fabric, Event Grid, and building prompt flow/LLM tools and RAG workflows.
Deployment	L458-L489	Deploying and operationalizing models and prompt flows to Azure ML (online/batch), including CI/CD, MLOps, blue‑green rollouts, RAG/LLM pipelines, and cross-workspace or registry 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
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
Profile and fix Azure ML pipeline performance	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipeline-performance?view=azureml-api-2
Diagnose and fix Azure ML pipeline reuse issues	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipeline-reuse-issues?view=azureml-api-2
Troubleshoot Azure automated ML experiment failures	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-auto-ml?view=azureml-api-2
Troubleshoot Azure ML batch endpoints and jobs	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-batch-endpoints?view=azureml-api-2
Troubleshoot data access issues in Azure ML SDK v2	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-data-access?view=azureml-api-2

Best Practices

Topic	URL
Mitigate overfitting and imbalance in Azure AutoML	https://learn.microsoft.com/en-us/azure/machine-learning/concept-manage-ml-pitfalls?view=azureml-api-2
Understand Azure ML model monitoring concepts and practices	https://learn.microsoft.com/en-us/azure/machine-learning/concept-model-monitoring?view=azureml-api-2
Optimize and manage Azure Machine Learning costs	https://learn.microsoft.com/en-us/azure/machine-learning/concept-plan-manage-cost?view=azureml-api-2
Design feature set transformations in Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/feature-set-specification-transformation-concepts?view=azureml-api-2
Author batch scoring scripts for AML batch deployments	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-batch-scoring-script?view=azureml-api-2
Tune Azure ML Kubernetes inference router performance	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-kubernetes-inference-routing-azureml-fe?view=azureml-api-2
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

Decision Making

Topic	URL
Choose between managed and custom network isolation in Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/concept-network-isolation-configurations?view=azureml-api-2
Choose 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 and plan migration from Azure ML v1 to v2	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-migrate-from-v1?view=azureml-api-2
Move Azure ML workspaces between subscriptions	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-move-workspace?view=azureml-api-2
Plan Azure ML network isolation architecture	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-network-isolation-planning?view=azureml-api-2
Map Azure ML v1 datasets to v2 data assets	https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-assets-data?view=azureml-api-2

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

Limits & Quotas

Topic	URL
Check regional availability for standard model deployments	https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoint-serverless-availability?view=azureml-api-2
Understand soft delete retention for ML workspaces	https://learn.microsoft.com/en-us/azure/machine-learning/concept-soft-delete?view=azureml-api-2
Manage Azure ML resource quotas and limits	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-quotas?view=azureml-api-2
Check Azure ML feature availability by sovereign cloud	https://learn.microsoft.com/en-us/azure/machine-learning/reference-machine-learning-cloud-parity?view=azureml-api-2
Supported VM SKUs for Azure ML managed online endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/reference-managed-online-endpoints-vm-sku-list?view=azureml-api-2
Plan capacity with Azure Machine Learning service limits	https://learn.microsoft.com/en-us/azure/machine-learning/resource-limits-capacity?view=azureml-api-2

Security

Topic	URL
Configure customer-managed keys for Azure Machine Learning	https://learn.microsoft.com/en-us/azure/machine-learning/concept-customer-managed-keys?view=azureml-api-2
Understand data encryption for Azure ML compute and storage	https://learn.microsoft.com/en-us/azure/machine-learning/concept-data-encryption?view=azureml-api-2
Understand data handling and privacy for Azure ML Model Catalog	https://learn.microsoft.com/en-us/azure/machine-learning/concept-data-privacy?view=azureml-api-2
Understand auth and RBAC for AML online endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints-online-auth?view=azureml-api-2
Plan enterprise security and governance for Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/concept-enterprise-security?view=azureml-api-2
Secret injection concepts for AML online endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/concept-secret-injection?view=azureml-api-2
Understand secure network traffic flow for Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/concept-secure-network-traffic-flow?view=azureml-api-2
Network isolation concepts for AML managed endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/concept-secure-online-endpoint?view=azureml-api-2
Manage vulnerabilities in Azure Machine Learning images	https://learn.microsoft.com/en-us/azure/machine-learning/concept-vulnerability-management?view=azureml-api-2

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

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
Author Azure ML CLI component-based pipelines	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-component-pipelines-cli?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
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
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

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
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 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 Hugging Face models to Azure ML endpoints	https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-models-from-huggingface?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