Domino 4.4 Gives Data Scientists the Tools to Reach Their Potential

Domino Data Lab, provider of the leading Enterprise MLOps platform, is releasing Domino 4.4, adding enhancements that re-imagine the data science workbench.

Domino 4.4 introduces several new features, including Durable Workspaces and CodeSync, which support a more productive way for data scientists to work.

Data scientists are driving the next generation of value for their companies and deserve an experience that unleashes their full potential—from richer research/experimentation to automated model monitoring and all points in between. Domino gives them the freedom and flexibility to develop, deploy, manage, and monitor models at scale,” said Bob Laurent, senior director of product marketing at Domino Data Lab.

This latest update delivers several key new advantages, Laurent said. First, it reduces the amount of manual and mundane work that data scientists need to do so they can maximize their productivity.

Second, it gives them the flexibility to work the way they want to work, with self-serve access to the tools and data they need.

Finally, Domino 4.4 provides tighter integration with the business/production systems that enable data science work to be done at enterprise scale, and that have an impact on business processes.

Durable Workspaces is a new feature allowing data scientists to run multiple development environments at the same time, with data and other artifacts that persist across sessions. This practice allows them to maximize productivity, eliminate lost work, and save infrastructure costs by starting, stopping, and resuming workspaces as needed, Laurent said. 

Another new feature, CodeSync allows data scientists to work seamlessly with the modern IT stacks used across the business. It improves upon the market-leading reproducibility capabilities of Domino to help data scientists save, find and reproduce work, and engage in version-controlled, code-based collaboration with other team members, Laurent explained. 

In addition, External NFS Volumes can now be mounted directly to the Domino file system to expedite access to local data. Data scientists can connect to and utilize more types of data outside Domino for greater experimentation, without moving it around as you have to do with inflexible cloud vendor tools.

“Domino provides code-first data scientists with self-serve access to the tools and scalable compute they need to be more productive. Domino lets them work the way they want to work—allowing them to get to work faster and be productive immediately, with no more wasted time on DevOps issues to solve infrastructure challenges,” said Laurent.

According to Laurent, new and novice data scientists will onboard faster and begin contributing in meaningful ways sooner. Domino removes the “cold-start” barriers (e.g., setting up environments, managing libraries, spinning up workspaces, finding previous work) so they can get to work faster.

Looking to the future, the company plans to continue to invest in four major areas:

  • Openness & Flexibility so data science teams can use any tools & infrastructure they want, now and in the future. IT can consolidate siloed technology stacks and reduce support burden.
  • Reproducibility & Collaboration to make it easier to reuse prior experiments and collaborate across different tools to harvest collective wisdom, compare results, and expedite projects.
  • Model Velocity to professionalize data science through common workflows that reduce friction and accelerate the end-to-end lifecycle from ideation to production.
  • Enterprise Scale to establish a data science system of record that captures all of the elements required to safely and universally scale data science across an organization.

“We believe that over the next decade, the companies that will beat competitors, drive unprecedented growth, and upend industries will be model-driven businesses—the ones that put models at the heart of their business," said Laurent. “To create these models, companies are undertaking large data science efforts to provide insights about what customers want to buy, how much inventory to keep, what products to develop, and more. With data science, a business doesn’t just plan for now; it can plan for next.”

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