Quickly reproduce work
Recreating prior work or delivering artifacts for audit, compliance or regulatory reporting can become a multi-month effort without a central, consistent manner of documenting projects.
Domino automatically tracks changes to code, data, tools, and packages through continual version control. These are captured in Durable Workspaces that allow data scientists to instantly roll back to or recreate the exact experiment environment used to create a model. This streamlines audit, governance, compliance, and regulatory reporting.
MLFlow integration simplifies machine learning lifecycle management. It lets data scientists track, reproduce, and share machine learning experiments and artifacts within their Domino projects, while Domino's security layer ensures metrics, logs, and artifacts are secured.
Easily find and reuse prior work
Without a way to effectively review prior work, it's easy to duplicate work, particularly when there are multiple teams of data scientists. There is also a high risk of lost institutional knowledge when key team members leave.
By capturing all data science artifacts in a central repository Domino captures all data science IP, including all activity on a project which provides critical context when it is next used. Data scientists can easily search to find prior work on a topic so they don’t reinvent the wheel and can rapidly compound knowledge on a topic.
Track progress, set goals, and define best practices
Data science projects need management to deliver expected business outcomes, like any other mission-critical activity.
With Domino, project goals are transparently tracked to measure business value. You can easily track progress and resolve blockers as well as establish custom project stages to instill consistent patterns and practices across the team. Git and Jira integration makes it easy to integrate data science into broader enterprise project processes.
System of Record features
Durable workspaces
Git and Jira integration
Central repository with version control
Auditability and governance
Model lineage
Projects portfolio
Assets portfolio
Project goals and stages
Considering building a data science platform?
It’s tempting to think that building a basic platform that centralizes infrastructure and tools will help you scale data science. But it’s not that simple. To safely and universally scale data science, you need a platform that provides orchestration, security, governance, collaboration, knowledge management, and self-service capabilities across the data science lifecycle.
One of the most important features is the ability to document work, maintaining a project’s artifacts and history for both research and auditing purposes.
Rick Bischoff
Chief Data Scientist
Frequently Asked Questions
How does Domino help with audit or regulatory compliance?
All project artifacts are tracked in Domino along with the exact environment used during model development. In just a few clicks all aspects of the model are instantly available.
How does Domino support project management in JIRA?
Domino's Jira integration allows for common Jira actions such as creating/editing goals and changing statuses to be performed in a Domino project. For more information, see the documentation on our Jira integration
How does Domino support Git repositories?
Domino's Git integration allows for adding, accessing, and committing changes to the content to both public and private repositories. For more information, see the documentation on our Git integration.