Domino orchestrates all data science artifacts, including infrastructure, data, and services on AWS and across hybrid-/multi-cloud environments — adding critical capabilities to Amazon SageMaker to meet the governance needs of Enterprise code-first data science teams.
Code-first data science teams benefit from a flexible, collaborative, and reproducible research environment, with self-service access to powerful AWS infrastructure and services within the governance of IT.
Domino runs on Amazon Elastic Kubernetes Service (EKS). Domino is an AWS Advanced Technology Partner, with both ML and Financial Services competencies.
Remove DevOps barriers with pre-built data connectors for Amazon S3, Redshift, and all of your common sources. Improve performance, lower costs, and enhance security with AWS AI Chips, Inferentia and Trainium, in Domino.
Deploy models in Amazon SageMaker for scalability and low-latency hosting. Deploy SageMaker models in Domino to monitor model performance across tools and teams.
Collaborate on and manage projects across teams. Orchestrate the AI stack, including multiple IDEs (Jupyter, R, SAS, MATLAB), packages, and compute frameworks (Spark, Ray, Dask, MPI). Industrialize AI with full reproducibility and shared best practices.
Orchestrate AWS infrastructure, data, and services for data scientists.
Host on AWS: AWS-Certified
Get your team on Domino in hours with Domino Cloud on AWS, available on AWS Marketplace.
Alternatively, deploy Domino in a dedicated VPC in the AWS region of your choice, including GovCloud. Domino's Kubernetes-ready platform runs on Amazon EKS, providing field-tested deployment patterns for ease of management.
Domino is an AWS Machine Learning Competency Partner, validated since 2017.
Pre-built Connectors: Seamless AWS Data Access
Instantly connect to popular data stores — including Amazon Redshift, S3, and others — with Domino Data Sources. Domino supports data access in the cloud, across multiple clouds, or on-prem — so your AI teams can innovate freely.
Securely store connection properties with per-user and service account credentials.
Self-Serve Infrastructure: Automate DevOps
Use the tools and packages of your choice, including Amazon SageMaker, Jupyter, RStudio, SAS, MATLAB, Anaconda, and more — with support across compute frameworks (i.e., Spark, Ray, and Dask).
Abstract away DevOps complexity so your data scientists can focus on innovation. Provide self-serve, easy access to Amazon EC2 machines, including AWS Inferentia and AWS Trainium, to run experiments faster to accelerate model development.
Models Anywhere: Flexible Deployment
Export models for deployment in Amazon SageMaker for scalable and low-latency hosting. Access models from SageMaker and SageMaker Autopilot, with Domino's Governance and model monitoring capabilities.
Amazon SageMaker Services/Features Complimentary to Domino Data Lab: SageMaker Data Wrangler, SageMaker Batch Transform, SageMaker Data Labeling, SageMaker AutoPilot, SageMaker Neo, SageMaker Edge Manager, SageMaker Feature Store, SageMaker Clarify, SageMaker Serverless Inference, SageMaker Asynchronous Interface.
Domino Data Lab is a founding member of the AWS Machine Learning Competency Partners, validated since 2017.
AWS has identified Domino as one of the key innovators in helping organizations extract real value from their data scientist investments.
Domino Data Lab is also an AWS Financial Services Competency Partner due to Domino's large footprint in banking, insurance, and algorithmic trading.
Playbook
Responsible AI calls for a proactive approach to identifying and mitigating business, legal, and ethical risks to deliver tangible business value.
AWS Storage Blog
Domino’s platform leverages the scale, security, reliability, and cost-effectiveness of AWS cloud computing coupled with Amazon EFS. Together they orchestrate all data science artifacts, such as AWS infrastructure, data, and services.
Webinar
Featuring experts from Domino and AWS, learn how AI governance can reduce risks, accelerate innovation, and deliver on the full potential of AI.
