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Domino unifies infrastructure and data to drive innovation, enhance control, and lower costs for all data science and AI initiatives. It lets you empower data science teams with the tools they love and with the governance guardrails to ensure safe, compliant, and cost-effective AI. Domino helps IT deliver the promise of AI value with the most flexible, scalable, and safe architecture for your AI operations.
  • Improve productivity: Deliver self-serve tools, secure access to data anywhere, and standardized computing in IT-governed sandboxes.
  • Run AI anywhere: On hybrid, multi-cloud, or on-prem to optimize costs, ensure compliance, and avoid vendor lock-in.
  • Slash operational costs: Simplify AI management by unifying commercial and open-source tools, disparate stacks, and data silos on one future-ready platform.
Domino machine learning ecosystem

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How do we do it?

Domino is an open system that provides self-service access to data and tools, enables the reuse of materials, and facilitates collaboration with other teams in your organization while enforcing best practices, enhancing knowledge, and improving efficiency.
  • Data: Domino connects to external data sources such as databases, data warehouses, and data lakes. The complete list of supported data sources is available under Data Source Connectors.
  • LLMs: Our deep learning models are trained on extensive datasets for language processing tasks. Based on their training data, they generate new text that mimics human language.
  • Software: Domino can be connected to GitHub, MLflow, and Sagemaker. This enables seamless integration of your data science workflows and allows users to track progress on data science projects.
  • Languages: Domino allows data scientists to use their preferred languages and tools, such as Python, SAS, Matlab, and R.
  • IDEs and Tools: You can use IDEs and tools like Jupyter Notebook, JupyterLab, RStudio, VS Code, MATLAB, and SAS with Domino.
  • Packages and Libraries: We support a range of packages and libraries, including open-source options such as Python, R, TensorFlow, PyTorch, and others.
  • Compute Environments: You can add different environments to any Domino installation by visiting quay.io. Our documentation has step-by-step instructions for setting up these environments.

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Administering Domino

These sections provide some information for Domino CloudAdmins.
  • Platform management: Manage project files and basic project settings for users to make sure the needs of your organization are fulfilled.
  • User management: Learn how to manage users in Domino to control permissions, set up accounts, and track activity.
  • Operations: Monitor and optimize the performance of Domino systems.
  • Deployment configuration: Configure different aspects of the Domino Deployment, including IP whitelisting and node pools.