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The Domino platform provides components to scale data science.
- Openness & Flexibility. Domino supports an ecosystem of open-source/commercial tools and infrastructure. Data scientists have self-serve access to their preferred IDEs, languages, and packages so they can focus on data science, not infrastructure. It also allows IT to consolidate disparate tools onto a single platform to reduce costs and support burden, and provide governance across a wide variety of tools, packages, etc. Domino prevents vendor lock-in by ensuring access to new tools and compute frameworks that emerge.
- Reproducibility & Collaboration. Disparate tools and all types of data science artifacts (including code, package versions, parameters, comments, and more) are automatically tracked and integrated to establish full visibility, repeatability, and reproducibility at any time across the end-to-end lifecycle of every use case. Teams using different data science tools can compare experiment results and seamlessly collaborate on a project, with the ability to leverage valuable insights and harvest a flow of collective wisdom.
- Model Velocity. Domino supports the lifecycle from ideation to production to explore data, train models, validate, deploy, monitor, and repeat – all in a single platform. Domino enables companies to professionalize their data science through common patterns and practices, with workflows that reduce friction and accelerate the lifecycle within each step and across key transitions, so all people involved in data science can maximize their productivity and the impact of their work.
- Enterprise Scale. While disparate data science teams are free to use their preferred tools, packages, and infrastructure, all aspects of their work are centralized and orchestrated through Domino. This is so users can onboard quickly, find previous work, collaborate, and reproduce experiments. Domino establishes a system of record for data science to provide the security, governance, compliance, and all of the other elements that are required with the goal of enabling users to scale data science safely and universally across an organization.
- Supported: Connect to Multiple Data Sources
- Supported: Extend Existing Data Sources
- Supported: Interactive Data Analysis
- Supported: Multiple Model Development Languages and Tools
- Supported: Single platform for multiple model development
- Supported: Self-Service Model Delivery
- Supported: Flexible Model Publishing Options
- Supported: Security, Governance, and Cost Controls
- Supported: Collaboration
- Supported: Reproducibility
- Supported: Enterprise Scale
|Operating Systems||Not Applicable|