"The paid platform we used briefly—a unified data analytics platform—was too reliant on Apache Spark™ and couldn’t provide the support, security, or flexibility our data engineers, data scientists, and ML engineers needed.”
Senior Health Data Scientist
Empowering individuals to participate in better health outcomesLearn More
Integrated Governance & Reproducibility
Domino automatically tracks and versions all project assets, including data, code, software, compute, experiments, models, batch jobs, and apps. You can instantly roll back to or recreate the exact environment used to create a model to streamline audit, governance, compliance, and regulatory reporting.
Domino Projects allow you to easily set goals, track progress, and resolve blockers. Git and Jira integration makes it easy to integrate data science into broader enterprise project processes. With Domino, your scientists collaborate with one another for improved model quality and productivity.
Choice of Cutting Edge Development Tools
With Domino you can work with any notebook or IDE, including SAS and MATLAB, with no loss of native functionality. Need more power?
You can spin up ephemeral Spark, Ray, and Dask clusters without an administrator. Working on a complex deep learning job? You can add GPUs to any workspace with a couple of clicks – all thanks to the industry’s deepest support for NVIDIA DGX architecture and AI Enterprise.
Runs on any Platform with Open Access to Data
Domino runs in all major public clouds OR on-premises so you can use the computing platform that best meets your needs.
Domino fully supports Kubernetes, including all of the major distributions: EKS, AKS, GKE, VMware, Red Hat, and Rancher. All Domino workloads run in Kubernetes today and support autoscaling for efficient use of computing infrastructure.
You can work with diverse data from many different platforms, including relational databases, cloud databases, NoSQL databases, cloud storage, and more. Domino is data platform agnostic with connectors to a wide variety of different sources so data can remain where it is.
Integrated Model Deployment and Monitoring
Domino provides you with many deployment options, including prediction APIs, apps, and batch jobs. You can also export models as Docker images to CI/CD pipelines, AWS, or other infrastructure. Interactive apps created with Shiny, Dash, and Flask allow non-technical users to interact with models.
Models don’t work well forever – they degrade over time. That’s why it’s critical to monitor your models in production. Domino automatically collects instrumented prediction and ground truth data so you can monitor deployed models for data drift and accuracy. You can set notifications when quality checks exceed thresholds. When a model drifts, you can easily drill down into model features to quickly modify, retrain and redeploy models.