Accelerate research
Getting data scientists quickly to work on solving business problems is key for scaling data science and driving business impact. Domino’s data science workbench provides the flexibility and power that data scientists need to accelerate research. They are free to use the tools they want, on hardware optimized for the task at hand, in a governed and scalable environment that fosters reproducibility, reusability, and collaboration.
Domino lets you run data science and machine learning workloads across any compute cluster — in any cloud, region, or on-premises. MLFlow integration makes it easy for data science teams to run hundreds of machine learning experiments in parallel and easily compare the results, maximizing their productivity.
Integration with Feast provides a single source of truth for features in the organization, driving reuse, consistency, and reproducibility.
Easily export production models to infrastructure of choice
Business value doesn’t accrue until you get models into production. Without repeatable processes and automation to expedite the validation and deployment of models, organizations at best delay ROI and often never see the full potential of their data science work.
With Domino, you can accelerate processes to create, manage, scale, and secure production models. Models are easily deployed through scalable APIs, Apps, and Launchers, or exported as Docker images to CI/CD pipelines, AWS Sagemaker, or other infrastructure. Interactive, scalable Apps created with Shiny, Dash, and Flask make it easy for non-technical users to interact with models.
Automatically track data drift, model quality and other health statistics
Models left to their own devices can quickly derail a business. But many organizations struggle to effectively monitor models in production and efficiently remediate issues to keep models at peak performance.
Domino's integrated model monitoring provides a “single pane of glass” for observing traffic, drift, and health trends for all production models with out-of-the-box and custom metrics and KPIs. You will be automatically alerted when drift, divergence, and data quality checks exceed thresholds. When retraining is needed, it's easy to drill down to model features to modify, retrain and redeploy models quickly.
Maximize your model velocity
Understand how well you are positioned to achieve model velocity and get suggested areas of improvement by taking this free 10-minute assessment
Integrated Model Factory features
Self-Serve sandboxes
Hybrid and
Multi-Cloud
MLFLow Experiment management
Analysis templates
App publishing
Scalable model APIs
Docker image export
Integrated model monitoring
Our data scientists feel right at home on the Domino platform and appreciate how much freedom they have in experimenting in Domino.
Michael Eiden
Global Head of AI and Machine Learning
Frequently Asked Questions
What data science tools are supported in Domino's Workbench?
How does Domino detect model drift?
What models can be monitored in Domino?