Reduction in end-to-end
model lifecycle time
Hours saved per model on retraining and rebuilding
Hours saved for each new
data science environment
Annual hours saved due
to productivity gains
As a data scientist, it is frustrating to lack the infrastructure and tools you need, spend lots of time on tedious DevOps tasks, and search for work you know others have already done. You need freedom and flexibility to work with peers, do your best work, and become a data superhero in your organization.
Domino gives you the flexibility and freedom to support what you do best – explore, experiment, and solve complex business problems – and abstract away the technical hurdles that slow you down.
With self-serve access to preferred tools and compute, automatic reproducibility, and repeatable workflows to manage, develop, deploy, and monitor models at scale, Domino will make you more productive and amplify the impact of your work.
Domino gives us the openness and flexibility we need to seamlessly experiment and collaborate rapidly, using our choice of scalable compute and infrastructure.
Luca Foschini, Ph.D.
Co-founder and Chief Data Scientist
Empowering individuals to participate in better health outcomes
Evidation processes millions of daily data points from consumer wearables to create production-grade models in as little as eight weeks.Read their Story
Recommended Resources for Data Scientists
Domino Data Science Blog
Our popular blog is a regular stop for data scientists who want to keep-up-to-date on all of the latest techniques, tools, and best practices – to accelerate their work and their careers.
Pump Up Data Science Productivity with a Modern Workbench
Most workbenches aren’t built to handle the work of large data science teams. Understand the unique workbench capabilities that Domino provides to maximize productivity.
The Pros and Cons of Spark in a Modern Enterprise Analytics Stack
Get an overview of Spark’s strengths and weaknesses in the context of data science and machine learning workflows.