Expert knowledge for Azure Data Science Virtual Machines development including troubleshooting, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when managing DSVM images/tools, IaC deployment (Bicep/ARM), Key Vault secrets, MLflow, or GPU/Jupyter issues, and other Azure Data Science Virtual Machines related development tasks. Not for Azure Virtual Machines (use azure-virtual-machines), Azure Machine Learning (use azure-machine-learning), Azure Databricks (use azure-databricks), Azure HDInsight (use azure-hdinsight).
v1.0Latest
New~1.7kUpdated Jun 26, 2026
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-L69
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
L70-L74
Using MLflow on Azure DSVMs to track experiments, log metrics/artifacts, and integrate runs with Azure Machine Learning for centralized experiment management
Deployment
L75-L79
How to deploy Azure Data Science VMs using infrastructure-as-code, including Bicep and ARM templates, parameters, and configuration best practices.
This skill provides expert guidance for Azure Data Science Virtual Machines (DSVMs), covering troubleshooting, decision-making, architecture patterns, security, configuration, integrations, and deployment. It functions primarily as a documentation index that directs agents to Microsoft Learn articles and optional local reference files, using network access to fetch remote content via mcp_microsoftdocs or fetch_webpage tools.
Detected Capabilities
documentation fetching via networkremote content retrieval (mcp_microsoftdocs, fetch_webpage)file reading for local referencequery string parameter passing
Trigger Keywords
Phrases that MCP clients use to match this skill to user intent.
azure dsvm troubleshootingdeploy dsvm bicepdsvm mlflow trackingazure data science vm migrationdsvm security key vaultconfigure dsvm toolsdsvm gpu issues
Risk Signals
INFO
Requires network access to external Microsoft Learn domains
compatibility field and throughout skill
INFO
Uses mcp_microsoftdocs tool which may not be installed by default
compatibility field and 'How to Use' section
INFO
Fallback to fetch_webpage for remote documentation if mcp_microsoftdocs unavailable
'How to Use This Skill' section
Referenced Domains
External domains referenced in skill content, detected by static analysis.
github.comlearn.microsoft.com
Use Cases
Troubleshoot DSVM creation, package installation, or connectivity issues
Migrate Azure Data Science VMs from Ubuntu 18.04 to 20.04
Design scalable DSVM-based analytics environments for teams
Secure DSVM credentials with managed identities and Key Vault
Deploy Azure DSVMs using Bicep and ARM templates
Track ML experiments with MLflow on DSVMs
Reference preinstalled tools, frameworks, and languages on Azure DSVMs
Positive: Well-organized Category Index with clear descriptions and line number references
Positive: Comprehensive URL references across 7 categories covering the full DSVM lifecycle
Positive: Explicit guidance for agents on using read_file with line ranges and linked files
Positive: Version management awareness (suggests updates if metadata is >3 months old)
Positive: Clear tool availability recommendations (preferred vs. fallback)
Negative: Content between lines L35-L79 is referenced but not provided in the skill file—only headers exist
Negative: No supporting reference files (e.g., security.md, deployment.md) are present despite being referenced in instruction text
Negative: Metadata generated_at date (2026-04-12) appears to be in the future, creating confusion about actual skill age
Negative: Category Index line numbers (L35-L39, L40-L44, etc.) do not correspond to actual content—appears to be placeholder structure
Negative: Missing error handling guidance for when documentation endpoints are unavailable or network is unreachable
Model: claude-haiku-4-5-20251001Analyzed: Jun 26, 2026
Reviews
Add this skill to your library to leave a review.
No reviews yet
Be the first to share your experience.
Use MicrosoftDocs/azure-data-science-vm in your dev environment — a Developer account adds skills to your library and syncs them via the SkillRepo CLI.