{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-ms-toolsai-vscode-ai-remote-web","slug":"azure-machine-learning-remote-web","name":"Azure Machine Learning - Remote (Web)","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-ai-remote-web","page_url":"https://unfragile.ai/azure-machine-learning-remote-web","categories":["code-editors"],"tags":["__web_extension","AML","Azure Machine Learning","Azure ML","Deep Learning"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-ms-toolsai-vscode-ai-remote-web__cap_0","uri":"capability://code.generation.editing.remote.python.code.execution.on.azure.ml.compute.instances","name":"remote python code execution on azure ml compute instances","description":"Enables execution of Python scripts and notebooks directly on remote Azure ML compute instances through a browser-based VS Code Web interface. The extension establishes a persistent connection to the remote compute instance's Python runtime, allowing developers to run code, capture output, and debug without local environment setup. Execution happens entirely on the remote machine with results streamed back to the browser IDE.","intents":["Run Python scripts on cloud-hosted compute without downloading code locally","Execute long-running training jobs from a lightweight browser IDE","Debug Python code interactively on a remote machine with full environment access","Test code against remote datasets and dependencies without replication"],"best_for":["Data scientists working in Azure ML Studio who need lightweight IDE access","Teams using Azure ML as their primary ML platform","Developers requiring cloud-native development without local GPU/compute setup"],"limitations":["Compute instance must be actively running (incurs Azure compute costs)","No local code execution capability — all execution is remote-only","Network latency and bandwidth constraints apply to output streaming","Execution environment is limited to what's installed on the remote compute instance"],"requires":["Active Azure ML workspace in Azure subscription","Running Azure ML compute instance with network access","Azure ML Studio session authentication (inherited from ml.azure.com)","Browser with vscode.dev support"],"input_types":["Python source code (.py files)","Jupyter notebooks (.ipynb)","Terminal commands"],"output_types":["Console output (stdout/stderr)","Execution results","Debug information"],"categories":["code-generation-editing","remote-development"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote-web__cap_1","uri":"capability://tool.use.integration.remote.filesystem.traversal.and.file.editing","name":"remote filesystem traversal and file editing","description":"Provides read/write access to the remote compute instance's filesystem and mounted Azure fileshares through VS Code's file explorer interface. The extension maps the remote filesystem into the browser IDE's file tree, enabling developers to browse, open, edit, and save files directly on the remote machine without downloading them locally. Changes are persisted immediately to the remote filesystem.","intents":["Browse and edit training scripts stored on remote compute instance","Access data files and configuration stored in mounted Azure fileshares","Modify code and configuration files without local copies","Explore remote project structure and dependencies"],"best_for":["Teams collaborating on Azure ML projects with shared fileshares","Developers working with large datasets that shouldn't be downloaded locally","Organizations enforcing data residency (keeping files in Azure)"],"limitations":["Filesystem scope limited to compute instance and mounted fileshares only","Cannot access host browser filesystem or local machine storage","Large file operations may experience latency over network","No built-in file sync or conflict resolution for concurrent edits"],"requires":["Running Azure ML compute instance","Fileshares mounted to the compute instance (for fileshare access)","Read/write permissions on remote filesystem","Network connectivity to Azure ML compute instance"],"input_types":["Remote file paths","File content (text, code, configuration)"],"output_types":["File tree structure","File content","Write confirmations"],"categories":["tool-use-integration","remote-development"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote-web__cap_2","uri":"capability://automation.workflow.remote.terminal.command.execution","name":"remote terminal command execution","description":"Provides an interactive terminal window connected to the remote compute instance's shell environment, enabling developers to execute arbitrary commands, install packages, manage git repositories, and interact with the remote environment directly from VS Code Web. Terminal input/output is streamed bidirectionally between the browser and remote machine.","intents":["Install Python packages and dependencies on remote compute instance","Clone and manage git repositories from remote environment","Run shell commands and scripts on remote machine","Check system status, environment variables, and installed software on remote compute"],"best_for":["ML engineers managing compute instance environments","Teams using git-based workflows for model and code versioning","Developers needing package management without local environment replication"],"limitations":["Terminal commands execute with compute instance user permissions only","No sudo/elevated privilege access documented","Interactive terminal sessions may timeout if idle","Command output is limited by network bandwidth and streaming latency"],"requires":["Running Azure ML compute instance with shell access","Network connectivity to remote compute instance","Appropriate permissions for commands being executed"],"input_types":["Shell commands (bash/sh)","Terminal input (interactive)"],"output_types":["Command output (stdout/stderr)","Exit codes","Interactive terminal responses"],"categories":["automation-workflow","remote-development"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote-web__cap_3","uri":"capability://tool.use.integration.azure.ml.studio.integration.entry.points","name":"azure ml studio integration entry points","description":"Provides direct launch points from Azure ML Studio UI to open VS Code Web connected to a specific compute instance. The extension is accessible via two entry points: a 'VS Code Web' link in the compute instance's Applications column, and an 'Edit in VS Code Web' option in the notebook editor dropdown. These entry points automatically establish the remote connection without requiring manual URL construction or credential entry.","intents":["Launch VS Code Web directly from compute instance management UI","Open notebook in VS Code Web editor from Azure ML Studio notebook interface","Quickly switch from Azure ML UI to IDE without manual connection setup","Maintain single authentication context across Azure ML Studio and VS Code Web"],"best_for":["Azure ML Studio users who prefer IDE-based development","Teams using Azure ML's notebook interface who need IDE features","Organizations with Azure ML as primary ML platform"],"limitations":["Entry points only available through Azure ML Studio UI (no direct URL access documented)","Requires active Azure ML Studio session for authentication","Private preview status limits availability to authorized users only","No programmatic API for launching VS Code Web connections"],"requires":["Azure ML Studio access (ml.azure.com)","Active Azure ML workspace","Running compute instance (for 'VS Code Web' link to appear)","Private preview access (as of documentation date)"],"input_types":["UI clicks/selections in Azure ML Studio"],"output_types":["VS Code Web browser window connected to compute instance"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote-web__cap_4","uri":"capability://code.generation.editing.notebook.editing.in.browser.based.ide","name":"notebook editing in browser-based ide","description":"Enables editing of Jupyter notebooks (.ipynb files) in VS Code Web with syntax highlighting, cell execution, and output rendering. The extension provides a lightweight notebook editor experience in the browser without requiring local Jupyter installation, with notebook cells executed on the remote compute instance and results streamed back to the browser.","intents":["Edit Jupyter notebooks in VS Code Web instead of Azure ML Studio notebook editor","Execute notebook cells on remote compute with IDE-based editing experience","Leverage VS Code's code editing features (autocomplete, refactoring) for notebook cells","Switch between notebook and script editing in same IDE window"],"best_for":["Data scientists preferring VS Code's editing experience over Azure ML Studio notebook UI","Teams mixing notebook and script-based development","Developers wanting IDE features (search, refactoring) in notebook workflows"],"limitations":["Notebook editing experience may be less feature-rich than dedicated notebook editors","Cell execution depends on remote compute instance availability","Large notebooks may experience rendering latency in browser","No documented support for notebook-specific features (e.g., variable inspector, debugger integration)"],"requires":["Running Azure ML compute instance","Jupyter notebook file (.ipynb) on remote compute","Python kernel on remote compute instance"],"input_types":["Jupyter notebook files (.ipynb)"],"output_types":["Rendered notebook cells","Cell execution output","Modified notebook files"],"categories":["code-generation-editing","remote-development"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote-web__cap_5","uri":"capability://automation.workflow.git.repository.management.on.remote.compute","name":"git repository management on remote compute","description":"Enables cloning, pulling, committing, and pushing git repositories directly from the remote compute instance through VS Code's source control interface. The extension integrates git operations into VS Code Web's SCM panel, allowing developers to manage version control without local git installation or manual command-line git operations.","intents":["Clone model and training code repositories from remote compute","Commit and push code changes from remote development environment","Manage git branches and pull requests from VS Code Web","Maintain version control for experiments and model code without local copies"],"best_for":["Teams using git-based ML workflows and experiment tracking","Organizations enforcing code versioning for reproducibility","Developers collaborating on shared Azure ML projects"],"limitations":["Git operations depend on remote compute instance's git installation and configuration","SSH keys and git credentials must be configured on remote compute instance","No documented support for git credential caching or SSH agent forwarding","Large repository operations may experience network latency"],"requires":["Git installed on remote compute instance","Git credentials or SSH keys configured on remote compute","Network access to git repositories from compute instance","Running Azure ML compute instance"],"input_types":["Git repository URLs","Commit messages","Branch names"],"output_types":["Repository clone/pull results","Commit confirmations","Git status information"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote-web__cap_6","uri":"capability://code.generation.editing.browser.based.development.environment.for.azure.ml","name":"browser-based development environment for azure ml","description":"Provides a complete development environment (code editor, terminal, file explorer, debugger) accessible entirely through a web browser (vscode.dev) without local VS Code installation. The extension extends VS Code Web's capabilities to support remote Azure ML compute instance connections, enabling full-featured IDE access from any browser without downloading or installing software locally.","intents":["Develop ML models from any device with a browser (Chromebook, tablet, thin client)","Access development environment without local software installation","Maintain lightweight client setup for organizations with security restrictions","Enable quick context switching between multiple Azure ML projects"],"best_for":["Organizations with BYOD policies or restricted local installation","Teams working across multiple devices or locations","Developers preferring cloud-native development without local setup","Educational institutions and training programs"],"limitations":["Browser performance and memory constraints may limit large file editing","Network connectivity required at all times (no offline development)","Browser-based IDE may lack some VS Code desktop features","Debugging experience may be limited compared to desktop VS Code"],"requires":["Modern web browser with vscode.dev support","Stable internet connection","Azure ML workspace and compute instance","No local VS Code installation required"],"input_types":["Browser requests","Keyboard/mouse input"],"output_types":["Browser-rendered IDE interface","Code editor content","Terminal output"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote-web__cap_7","uri":"capability://safety.moderation.inherited.azure.ml.studio.authentication.and.session.management","name":"inherited azure ml studio authentication and session management","description":"Automatically inherits authentication context from Azure ML Studio (ml.azure.com) session without requiring separate credential entry or API key management. The extension establishes remote connections using the existing Azure ML Studio authentication token, eliminating manual credential configuration and maintaining a single authentication context across both applications.","intents":["Launch VS Code Web without re-entering Azure credentials","Maintain single sign-on experience across Azure ML Studio and IDE","Eliminate credential management and API key configuration","Ensure authentication state is synchronized with Azure ML Studio session"],"best_for":["Organizations using Azure AD for identity management","Teams requiring single sign-on across Azure services","Users preferring minimal credential management"],"limitations":["Authentication depends on active Azure ML Studio session (no independent credential management)","Session timeout in Azure ML Studio may invalidate VS Code Web connection","No documented support for service principal or managed identity authentication","Multi-factor authentication behavior not documented"],"requires":["Active Azure ML Studio session (ml.azure.com)","Azure AD credentials with Azure ML workspace access","Browser cookies enabled for session persistence"],"input_types":["Azure AD credentials (inherited from Azure ML Studio)"],"output_types":["Authentication token","Remote connection establishment"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ms-toolsai-vscode-ai-remote-web__cap_8","uri":"capability://safety.moderation.private.preview.access.control.and.feature.gating","name":"private preview access control and feature gating","description":"Implements access control limiting the extension to authorized users during private preview phase. The extension is only available to internal Microsoft employees and external users who complete an onboarding form signup process, with access provisioned through Microsoft's extension marketplace. This gating mechanism prevents general availability until the extension reaches production readiness.","intents":["Control early access to experimental Azure ML development features","Gather feedback from selected users before general release","Manage support and documentation for limited user base","Validate Azure ML integration patterns with early adopters"],"best_for":["Microsoft internal teams and early adopter partners","Organizations participating in Azure ML preview programs","Teams willing to provide feedback on experimental features"],"limitations":["Not available for general public use (private preview only)","Requires onboarding form approval for external users","No guaranteed stability or backward compatibility (preview status)","Documentation and support may be limited during preview"],"requires":["Microsoft internal employee status OR","Approved onboarding form submission","Invitation/access grant from Microsoft"],"input_types":["Onboarding form submission"],"output_types":["Access grant/denial","Extension availability in marketplace"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["Active Azure ML workspace in Azure subscription","Running Azure ML compute instance with network access","Azure ML Studio session authentication (inherited from ml.azure.com)","Browser with vscode.dev support","Running Azure ML compute instance","Fileshares mounted to the compute instance (for fileshare access)","Read/write permissions on remote filesystem","Network connectivity to Azure ML compute instance","Running Azure ML compute instance with shell access","Network connectivity to remote compute instance"],"failure_modes":["Compute instance must be actively running (incurs Azure compute costs)","No local code execution capability — all execution is remote-only","Network latency and bandwidth constraints apply to output streaming","Execution environment is limited to what's installed on the remote compute instance","Filesystem scope limited to compute instance and mounted fileshares only","Cannot access host browser filesystem or local machine storage","Large file operations may experience latency over network","No built-in file sync or conflict resolution for concurrent edits","Terminal commands execute with compute instance user permissions only","No sudo/elevated privilege access documented","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.42,"quality":0.28,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:36.253Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=azure-machine-learning-remote-web","compare_url":"https://unfragile.ai/compare?artifact=azure-machine-learning-remote-web"}},"signature":"um8rFvbTYCO2Gn3EwCndCXywg5IUx9i6sEdudIejSVE7iiYbIV3FS3JdLN9d95weGx+nuxWXL7ygon2Mw/n7Cw==","signedAt":"2026-06-21T15:04:00.484Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/azure-machine-learning-remote-web","artifact":"https://unfragile.ai/azure-machine-learning-remote-web","verify":"https://unfragile.ai/api/v1/verify?slug=azure-machine-learning-remote-web","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}