Now with model registry!Learn more
Machine Learning Teams
Seamless data and model management, experiment tracking, visualization and automation, with Git as the single source of truth.
Combine the power of leading open-source tools:
Our experts will help you get started and answer any questions you may haveBook an onboarding call
See how Iterative Studio can make your ML teams more productiveExplore demo project
The application is invites-only, please, ask an admin to provide you access. If you already have an invite, please connect with your account:
- 7,000+Community members
- 250Open source contributors
- 14,000+Stars on GitHub
Built for ML researchers, practitioners, and managers
Model Registry for Machine Learning
Use your Git repositories to build a model registry with model versioning, lineage, and lifecycle management.
Enable model organization and discovery across all your ML projects. Manage model lifecycle with Git, unifying ML projects with the best DevOps practices. Use your favorite interface - GUI, CLI or API.
ML experiment tracking, visualization, and collaboration
Iterative Studio performs your ML bookkeeping automatically, to provide your team easy and efficient knowledge sharing and collaboration.
Get quick access to important metrics across multiple projects. Visualize and compare ML models. Use GitHub/GitLab/Bitbucket for ML teams, along with your own cloud and remote storage for your data and models.
Software engineering stack for ML teams
Apply proven best-practices across domains, and ensure optimum utilization of skills and resources.
Don’t reinvent the wheel. Use existing know-how of Git and CI/CD to keep your code, data and model connected at all times. DVC versions data and models right in Git repositories, and CML orchestrates resources in the cloud or Kubernetes.
Automation of your ML process
Iterate faster, and lower risks by transitioning to an intuitive no-code interface. Bridge the gap between data scientists, software engineers and managers, once and for all.
Mature ML teams reuse their code over and over again while tuning data and params. Iterate faster by transitioning to a no-code environment - use a visual UI to make and deploy changes instead of manually updating code each time.
On the roadmap
Keep an eye out for new features around model and data registries for your ML data to facilitate data discoverability and security.
Plans and pricing
Free for small teams up to 2 members
- Unlimited repositories
- Integration with popular Git providers
- Integration with on-premises GitLab
- Sharing projects
- Running experiments
- Plots visualization
- Data-centric comparison of experiments
- Integration with common cloud providers
- Granular Access Control
- Authentication via popular Git providers and emails
- Model registry NEW
$40 per team member monthly
Collaboration on Machine Learning projects for teams of any size
Everything in Free plan, plus:
- Unlimited collaborators
$99 per team member monthly
Basic plan with added MLOps services and support
Everything in Basic plan, plus:
- Priority support
- Customer success
- Quarterly review of MLOps goals
For a large teams collaboration with security requirements
Everything in Teams plan, plus:
- Flexible deployment (our VPC, your VPC, on-premises)
- Dedicated ML solution architect and professional services
We offer discounts to active DVC/CML teams, nonprofits, and educational institutions for Enterprise plan. Reach out to learn more.
|Price||$0||$40 per team member monthly||$99 per team member monthly||Custom price *|
|Team members||Up to 2 per team||Purchased amount||Purchased amount||Purchased amount|
|Hosting and installation||Free||Basic||Teams||Enterprise|
|Integration with GitLab.com, GitHub.com, Bitbucket.org|
|Integration with on-premises GitLab|
|Integration with on-premises GitHub and Bitbucket (requires on-premises Studio installation)|
|Flexible deployment |
(our VPC, your VPC, on-premises)
|Number of Git repositories that can be connected||Unlimited||Unlimited||Unlimited||Unlimited|
|Sharing projects with teams/public|
|Data-centric comparison of experiments|
|Integration with common cloud providers including AWS, GCP, Azure and Kubernetes|
|Data Catalog for structured (csv, parquet) and unstructured (images, audio) datasoon|
|Granular Access Control |
(Read, Write, Admin)
|Authentication via GitHub, GitLab and Bitbucket and email|
|Dedicated ML solution architect and professional services||Limited|
up to 2
per team member monthly
per team member monthly
Reach out to learn more.