Domino Data Lab Unleashes Model Velocity for Data Science Teams with Domino 5.0

Domino Data Lab, provider of an Enterprise MLOps platform, is releasing Domino 5.0 with new capabilities that unleash model velocity, a metric of how fast data science teams build and update models.

Domino 5.0 is also the validated and integrated with NVIDIA AI Enterprise, an end-to-end software suite optimized to run AI workloads with VMWare vSphere with Tanzu, on mainstream data center servers.

By making data scientists more productive and increasing collaboration and reuse of work, Domino 5.0 unleashes model velocity for data science teams, according to the vendor.

“Over the next decade, winning companies across industries will be the ones that weave data science into the fabric of their business and drive rapid continuous improvement of their models,” said Nick Elprin, CEO and co-founder of Domino Data Lab. “Domino 5.0 gives enterprises the modern platform they need to maximize their model velocity and the impact of their data science investment.”

Domino 5.0 introduces three new capabilities that address common challenges data science teams face: accessing compute infrastructure, collaborating using data sources, and productionizing models.

First, Autoscaling Clusters let data scientists spin up elastic compute clusters on demand with just a few clicks.

With support for Ray, Dask, and Spark, Domino lets data scientists choose their preferred compute framework without locking them into a single option.

Second, Data Connectors eliminate significant time wasted by data scientists finding and accessing data, including configuring the right tools to connect to it. Domino 5.0 streamlines that entire process, allowing data science teams to securely share and reuse common data access patterns, removing a major speed bump in the research process.

Third, Integrated Monitoring with Automated Insights unifies model development, deployment and monitoring to speed up the process of continuously improving models.

When deploying a model, Domino automatically creates the pipeline to capture prediction data and compare it to training data to detect drift.

When drift occurs, Domino lets data scientists easily launch a development environment with the original model materials, to investigate and redeploy it. Additionally, Automated Insights help data scientists rapidly diagnose drift by generating customized cohort analyses that highlight likely causes in an easy-to-consume report.

“As AI adoption grows, enterprises and research organizations around the world are seeking tools to help their teams collaborate more efficiently,” said Manuvir Das, head of enterprise computing at NVIDIA. “The combination of the NVIDIA AI Enterprise software suite with Domino Data Lab Enterprise MLOps provides IT teams with an integrated platform for accelerating AI development and deployment on their data center infrastructure.”

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