Data Science Workbench | Domino Data Lab

Accelerate Collaborative Research and Development

Domino’s data science workbench provides the flexibility and power that data scientists need to accelerate research and make breakthroughs that deliver real business impact. They can use the tools they want, on hardware optimized for the task at hand, in a governed and scalable environment that fosters reproducibility, reusability, and collaboration.

Use the Tools You Want with Self-Serve Access to Scalable Compute

Spin up Jupyter, RStudio, Zeppelin, SAS Studio, and other interactive workspaces with just a few clicks
Add and update libraries, tools, and algorithm packages to support rapid innovation
Link to Git-based repositories to quickly integrate existing code
Get visibility into tool usage from a centrally-managed and governed environment
Visit the Domino Data Science Blog – dedicated to helping data scientists accelerate their work and their careers

Run Computationally Intensive Experiments Simultaneously

Elastically scale compute resources, including CPU, GPU, and Spark clusters, on-demand without DevOps
Run hundreds of experiments in parallel, and easily compare the results
Iterate and collaborate on projects to expedite research breakthroughs
BLOG: 4 Ways to Successfully Scale Machine Learning

Automatically Track All Work and Progress Over Time

Easily find and reproduce past results, with tracked code, data, tools, packages, and compute environments
No more concerns over inconsistent versions and changes to the underlying data
Link your Domino project to a Jira ticket, and synchronize project status
Gain the freedom to collaborate, while establishing powerful governance capabilities
WHITE PAPER: How to Evaluate a Data Science Platform

We’re streamlining the development and deployment of deep learning models to mitigate supply chain risk, analyze manufacturing defects, predict maintenance needs, and more. Domino makes it easy for our data scientists to rapidly access NVIDIA GPUs so we can support complex use cases like training deep neural networks.

Mike Johnson

Lead Data Scientist

Lockheed Martin

Domino Data Lab Enterprise MLOps Platform | Customer stories

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