Domino's Enterprise MLOps Platform
Overcome the biggest challenges to data science at scale - infrastructure friction, productionization challenges, and a lack of collaboration with Domino’s Enterprise MLOps Platform.
Scale data science with Domino
The Domino Enterprise MLOps Platform provides:
- A System of Record to increase productivity through compounding knowledge and making work reproducible and reusable
- An Integrated Model Factory that lets you develop, deploy and monitor models in one place using your preferred tools and languages
- A Self-Service Infrastructure Portal for one-click, governed access to the data, tools, and compute you and your team need.
Typical customer results with Domino
faster model deployment
shorter model lifecycle
System of Record
Compound knowledge instead of reinventing the wheel
Domino captures all data science work in a central repository, so your team can easily find, reproduce and reuse work. Gone are the days of data scientists starting projects from scratch only to find out another team member is working on the same problem. Instead, knowledge is compounded with reusable code, artifacts, and learnings from previous experiments, integrated project management capabilities, and the ability to replicate development environments.Learn More about System of Record
Integrated Model Factory
Increase model velocity
Domino supports the end-to-end data science lifecycle from ideation to production: explore data, train machine learning models, validate, deploy, and monitor. Then rinse and repeat – all in one place. Enable repeatable processes and workflows that get models into production faster, enable automated monitoring, retrain and republish models more often, and much more – all designed to reduce friction and increase model velocity on your way to becoming a model-driven business.Learn More about Integrated Model Factory
Self-service infrastructure portal
Powerful infrastructure at your fingertips
Domino automates the time-consuming DevOps tasks required for data science work at scale. With only a few clicks you can spin up a development sandbox pre-loaded with your preferred tools, languages, and compute, including popular distributed compute frameworks. Jump between environments, bring in more data, compare experiments, deploy and iterate on models, and just be more productive with a platform optimized for code-first data science teams.Learn More about Self-Service Infrastructure Portal
“We’ve implemented a multi-cloud strategy, along with Domino’s Enterprise MLOps platform, to increase our model velocity so we can address customer needs in a quarter of the time it used to take us.”
Head, Data Science
All users benefit from Domino
Data Science Leaders
Teams can find and reproduce work to maximize productivity. You can manage work and standardize processes to rapidly deliver business value.
Self-serve access to your preferred tools and infrastructure lets you develop and deploy models faster.
Support data science while increasing governance and reducing the cost of supporting data science infrastructure
Data science and machine learning engineering platforms promote code-first development to build and support ML models used in critical business applications.
2022 Gartner Market Guide for DSML Engineering Platforms
Domino Data Lab is recognized as a Representative Vendor in this important industry report.Get the Report
Frequently Asked Questions
What is included with the Domino Enterprise MLOps Platform?
Along with the Domino Enterprise MLOps platform, a standard license includes:
- Unlimited consumer licenses (ie apps, web reports)
- User and admin training sessions
- New platform releases during the license period
- Technical support and advice
- Installation for Certified Deployment configurations
What is the price of the Domino Enterprise MLOps Platform?
Domino Data Lab offers a wide range of pricing options based on your business’s needs. Reach out to our sales team here to discuss pricing options and to request a quote.
Why should we consider Domino instead of building a platform ourselves?
Many organizations start by building their own data science platform. They focus on solving infrastructure challenges but don’t think about what an ideal end-to-end process should be. The cost of maintenance, the value of product feedback from multiple parties, and the importance of features beyond simple infrastructure orchestration are often underestimated. We have outlined the true costs of building a data science platform in this whitepaper.