Azure Data Science Virtual Machines Skill

This skill provides expert guidance for Azure Data Science Virtual Machines. Covers troubleshooting, decision making, architecture & design patterns, 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	L35-L39	Diagnosing and resolving common Azure Data Science VM issues, including VM creation, package/environment errors, Jupyter access, GPU/driver problems, and performance or connectivity failures.
Decision Making	L40-L44	Guidance for upgrading Azure Data Science VMs from Ubuntu 18.04 to 20.04, including migration steps, compatibility considerations, and preserving tools/configurations.
Architecture & Design Patterns	L45-L50	Designing scalable DSVM-based analytics environments, including architecture patterns, shared VM pools, team workflows, and resource management for data science teams.
Security	L51-L56	Managing identities and credentials for Azure DSVMs, including shared identity setup, managed identities, and securing secrets with Azure Key Vault.
Configuration	L57-L70	Details of all preinstalled tools, frameworks, languages, and images on Azure DSVMs, including ML/deep learning, data ingestion, dev/productivity tools, and release/version info.
Integrations & Coding Patterns	L71-L75	Using MLflow on Azure DSVMs to track experiments, log metrics/artifacts, and integrate runs with Azure Machine Learning for centralized experiment management
Deployment	L76-L80	How to deploy Azure Data Science VMs using infrastructure-as-code, including Bicep and ARM templates, parameters, and configuration best practices.

Troubleshooting

Topic	URL
Troubleshoot known issues on Azure DSVM	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/reference-known-issues?view=azureml-api-2

Decision Making

Topic	URL
Migrate DSVM from Ubuntu 18.04 to 20.04	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/ubuntu-upgrade?view=azureml-api-2

Architecture & Design Patterns

Topic	URL
Design team analytics environments with DSVM	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-enterprise-overview?view=azureml-api-2
Architect shared DSVM pools for analytics teams	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-pools?view=azureml-api-2

Security

Topic	URL
Configure common identity for multiple DSVMs	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-common-identity?view=azureml-api-2
Secure DSVM credentials with managed identities and Key Vault	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-secure-access-keys?view=azureml-api-2

Configuration

Topic	URL
Review data platform tools on Azure DSVM	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tools-data-platforms?view=azureml-api-2
Use preinstalled ML tools on Azure DSVM	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tools-data-science?view=azureml-api-2
Check deep learning frameworks on Azure DSVM	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tools-deep-learning-frameworks?view=azureml-api-2
Identify development tools available on DSVM	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tools-development?view=azureml-api-2
Use data ingestion tools on Azure DSVM	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tools-ingestion?view=azureml-api-2
Review programming languages preinstalled on DSVM	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tools-languages?view=azureml-api-2
Leverage productivity tools on Azure DSVM	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tools-productivity?view=azureml-api-2
Reference tools installed on Ubuntu DSVM	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/reference-ubuntu-vm?view=azureml-api-2
Review Azure DSVM release changes and versions	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/release-notes?view=azureml-api-2
Review preinstalled tools on Azure DSVM images	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/tools-included?view=azureml-api-2

Integrations & Coding Patterns

Topic	URL
Track DSVM experiments with MLflow and Azure ML	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/how-to-track-experiments?view=azureml-api-2

Deployment

Topic	URL
Deploy Azure DSVM using Bicep templates	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tutorial-bicep?view=azureml-api-2
Deploy Azure DSVM with ARM templates	https://learn.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-tutorial-resource-manager?view=azureml-api-2