Team Collaboration Features

Overview

Transium provides comprehensive collaboration tools to help teams work together effectively on AI projects. From shared workspaces to version control, these features enable seamless teamwork across distributed teams.

Workspaces

Organize your team's work with shared workspaces:

Creating a Workspace

# Create a new team workspace
workspace = client.workspaces.create(
name="ml-research-team",
description="Machine Learning Research Projects",
members=["alice@company.com", "bob@company.com"]
)

Workspace Features

  • Shared model repositories
  • Collaborative experiment tracking
  • Team resource quotas and billing
  • Centralized project management

Permission Management

Control access to resources with granular permissions:

Permission Levels

Owner: Full control over workspace and all resources
Admin: Manage members, resources, and settings
Developer: Create and modify models and experiments
Viewer: Read-only access to workspace resources

Managing Permissions

# Add team member with specific role
workspace.add_member(
email="newmember@company.com",
role="developer"
)

# Update member permissions
workspace.update_member_role(
email="member@company.com",
role="admin"
)

Experiment Sharing

Share experiments and results with your team:

Experiment Tracking

  • Shared experiment logs and metrics
  • Collaborative model versioning
  • Team-wide experiment comparisons
  • Automated experiment notifications

Sharing Experiments

# Share experiment with team
experiment.share(
workspace_id="ml-research-team",
permissions=["read", "comment"]
)

# Add experiment comment
experiment.add_comment(
message="Great results! Let's try with higher learning rate."
)

Model Collaboration

Collaborate on model development and deployment:

Model Repositories

  • Shared model storage and versioning
  • Collaborative model reviews and approvals
  • Team-based model deployment workflows
  • Model performance monitoring and alerts

Model Review Process

# Submit model for team review
model.submit_for_review(
reviewers=["senior-ml-engineer@company.com"],
message="Ready for production deployment"
)

# Approve model deployment
model.approve(
reviewer="senior-ml-engineer@company.com",
comments="LGTM - performance metrics look good"
)

Communication Tools

Built-in Messaging

  • Team chat channels
  • Direct messaging
  • Experiment and model discussions
  • Automated status updates

External Integrations

  • Slack notifications and commands
  • Microsoft Teams integration
  • Email alerts and summaries
  • Webhook notifications

Resource Sharing

Share compute resources and manage team quotas:

# Configure team resource quotas
workspace.set_quotas(
gpu_hours_per_month=1000,
storage_gb=500,
max_concurrent_jobs=10
)

# Share GPU resources
workspace.enable_resource_sharing(
allow_gpu_sharing=True,
priority_users=["lead-researcher@company.com"]
)

Best Practices

  • Establish clear naming conventions for experiments and models
  • Use descriptive commit messages and experiment notes
  • Regularly review and clean up unused resources
  • Set up automated notifications for important events
  • Document model requirements and deployment procedures
  • Conduct regular team sync meetings to discuss progress
  • Use tags and labels to organize shared resources

Getting Started

Ready to set up team collaboration? Contact our team at hello@transium.com to enable collaboration features for your organization.