Domino Data Lab

Founded by Nick Elprin in 2013

Build, deploy, and govern machine learning models across your enterprise infrastructure

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You can use Domino to develop, deploy, and monitor machine learning models in a centralized environment. The system allows data science teams to write code using Python, R, or SAS while accessing elastic computing resources across clouds or on-premise hardware. You can track experiments automatically to guarantee reproducibility across every project. Teams can package models into interactive applications, automate governance workflows, and enforce regulatory policies before deployment. It also tracks model performance in production to prevent drift and maintain strict audit trails for enterprise compliance.

What Domino Data Lab does

Track code, data, and compute environment changes automaticallyDeploy models as production APIs or interactive appsEnforce compliance and governance policies before deploymentRun distributed machine learning training jobs across cloud resourcesAudit model performance and risk across regulatory lifecyclesCollaborate across data teams using shared repositories and notebooksCentralized statistical computing environments for SAS, R, and PythonAutomatic experiment tracking and environment reproducibilitySelf-service access to distributed compute infrastructureBuilt-in governance controls and regulatory compliance auditingDirect deployment of models as APIs and interactive web appsAutonomous agent design and lifecycle trackingModel risk management and validation reporting