Domino 4.4 Release Designed to Boost Data Scientists’ Productivity
Data science workbench in Domino 4.4 reduces the amount of manual and mundane work that data science teams must do to manage code and gain access to data.
Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.
Domino Data Lab, provider of a popular enterprise MLOps platform , released Domino 4.4. This update introduces several features, including Durable Workspaces and CodeSync designed to support a more productive way for data scientists to work.
The newly imagined data science workbench in Domino 4.4 reduces the amount of manual and mundane work that data science teams must do to manage code and gain access to data. It integrates seamlessly with modern IT stacks to enable data science work to be done within the enterprise. Also, governed and secure access is provided to new and novice users, with guardrails that protect against lost work, unintentional runs, and significant cloud compute bills.
Domino’s ability to accelerate the time to onboard new team members equates to significant savings for companies -- an estimated 200 hours per year per data scientist and nearly $1 million over the course of three years, according to a recent study by Forrester Consulting, “The Total Economic Impact (TEI) of the Domino Enterprise MLOps Platform.” This study cited Domino’s intuitive interface and support for familiar tools as the reason why little or no training is necessary and on average, each data scientist is productive in just one day instead of two weeks in prior environments.
Centralizing Data Science Work
The Domino Enterprise MLOps platform centralizes data science work and infrastructure across the enterprise for collaborative building, training, deploying, and managing of models faster and more efficiently. With Domino, data science teams are free to innovate faster, reuse each other’s work, and collaborate more efficiently -- without the need for IT teams to manage and govern infrastructure.
Domino 4.4 delivers new capabilities, including:
Durable workspaces. This new feature releases teams from the confines of a single workspace, where they must do their work, commit the results, and close the workspace before moving on to the next task. Durable Workspaces enable data scientists to operate with multiple development environments open at the same time, with the ability to commit work to version control whenever they want. This new way of working allows data science teams to:
- Maximize productivity by running multiple simultaneous environments, with data and other artifacts that persist across sessions
- Eliminate lost work with robust and resilient sandboxes for experimentation that allow work to be committed to version control whenever data scientists want
- Save infrastructure costs by stopping, editing, and resuming workspace configurations to match the task at hand
CodeSync. Domino automatically tracks all aspects of experimentation so data science work is reproducible, discoverable, and reusable -- increasing the throughput of data science teams and mitigating regulatory risk. Domino’s market-leading reproducibility capabilities are now enhanced by CodeSync to provide native integration with widely used Git repositories. This new technology allows data science teams to:
- Improve compliance and governance by integrating data science code across a company’s continuous integration and continuous delivery (CI/CD) workflows in an established enterprise Git server
- Simplify the versioning of code, with more control over syncing, branching, and merging of complex workflows
- Enhance team collaboration and their ability to reproduce work with code, data, and other materials needed for data science in a centralized system
External NFS volumes. With Domino 4.4, external Network File System (NFS) volumes can now be mounted directly to the Domino file system to expedite access to local data. By eliminating the need to copy data into the cloud, and the associated security risk, data scientists can:
- Connect to and utilize more types of data outside Domino for greater experimentation
- Work seamlessly with immediate access to data without moving it around as required with inflexible cloud vendor tools
- Improve the performance of storage latency-sensitive workloads
Encryption in Transit. Sometimes it is necessary to move data between sites, which places it at risk if communications are intercepted. With Domino 4.4, teams can leverage encryption in transit to reduce risk using Transport Layer Security (TLS), an industry-standard method for encrypting data in transit.
For details, visit dominodatalab.com.