Reduction in end-to-end
model lifecycle time
Faster onboarding for
new data scientists
Decrease in model
ROI as measured by
Let’s face it, empowering your team to scale data science is hard. Outdated tools and lots of manual work make for low morale and productivity. Models don’t get deployed, or make bad predictions. Teams duplicate work with low visibility to and reuse of other data science work. As a leader, you lack visibility to model portfolios and their performance. Without the right technology, data science remains experimental and ad hoc. In fact, our recent survey found that 68% of analytics professionals believe it’s “somewhat difficult” and 37% claim it’s “very to extremely difficult” to get models into production to impact business decisions.
Domino centralizes data science infrastructure so you can scale how you manage teams and projects, improve collaboration, and accelerate project delivery.
Before we had Domino, it took eight weeks to get access to a GPU. Now it's just a push button away. Those savings add up across thousands of employees at scale, and that really makes a difference.
Senior Manager of AI and AiMLabs
Pushing the Boundaries of Rocket Science with Data Science
See how Domino and NVIDIA are helping Lockheed Martin create an MLOps framework to bring development, security, and operations best practices to machine learning – and achieve a 10 to 100X efficiency gain for data scientists.Watch the Webinar
Domino has made it easier for users across the global enterprise, using different tools and with varied backgrounds and skill sets, to work with each other, leverage past work, and collaborate quickly.
Former Data Science Center of Excellence Lead
Helping Farmers Grow Crops More Efficiently through Innovation and Sustainability
Bayer estimates that Domino has helped generate $100 million in NPV over three years.Read their Story
Model monitoring in Domino saves us significant time previously spent on maintenance and investigation, and enables us to monitor model performance in real-time and compare it to our expectations.
Head of Data Science Center of Excellence
Giving Homeowners Answers About Insurance Coverage in Seconds with Model-driven Policy Approvals
Domino reduces insurance coverage answers and approvals from 4 days to under 2 seconds.Read their Story
Estimate Your ROI from Domino
Consider the numbers:
- Domino saves over 200 hours per year per data scientist
- A data scientist with Domino is productive in 1 day vs. 2 weeks
- Domino saves an average of 40 hours for each model validation
- Domino reduces rebuilding time by 60 hours per model
Answer 6 questions and get a high-level estimate of the value Domino can deliver to YOUR organization based on the results of the Forrester Total Economic Impact (TEI) of the Domino Enterprise MLOps platform.
Recommended Resources for Data Science Leaders
The Practical Guide to Accelerating the Data Science Lifecycle
The need to accelerate and optimize the Data Science Lifecycle (DSLC) to achieve high model velocity has always been important. It became absolutely critical for businesses in the wake of the pandemic. Understand the DSLC, learn how to accelerate it, and how MLOps plays a critical role.
Model Velocity: The Key to Accelerating Your Model-driven Business
Model Velocity measures your company's ability to traverse the end-to-end model development and deployment process rapidly, repeatedly, and consistently.
The Complete Guide to Enterprise MLOps
Explore the underlying technologies and guiding principles found in Enterprise MLOps, and get recommendations for removing technical and non-technical barriers to success.