Domino 5.0 introduces groundbreaking new capabilities to help enterprises dramatically accelerate data science at scale, following validation as the first Enterprise MLOps platform for NVIDIA AI Enterprise

Domino Data Lab, provider of the leading Enterprise MLOps platform trusted by over 20% of the Fortune 100, introduced Domino 5.0 with groundbreaking new capabilities that unleash model velocity, a metric of how fast data science teams build and update models, by solving common challenges related to compute infrastructure, data, and productionization of models. Domino 5.0 is also the first Enterprise MLOps solution 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.

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As companies invest in data science and machine learning, many fail to realize the impact they expect because existing processes, cultures and technologies make it hard for data scientists to rapidly and safely develop and deliver models. New capabilities that unify model development, deployment and monitoring make Domino 5.0 the only platform that facilitates the end-to-end data science lifecycle while giving data scientists the flexibility to use their preferred tools. By making data scientists more productive and increasing collaboration and reuse of work, Domino 5.0 unleashes model velocity for data science teams.

“Through our best practices and use of Domino’s Enterprise MLOps platform we’ve been able to accelerate model deployment by as much as six times,” said Jacob Grotta, General Manager of Banking Operating Unit, Moody’s Analytics. “This increase in model velocity significantly improves our ability to get information into the hands of our clients faster and solve their challenges in ways that would previously have been impossible.”

“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 groundbreaking 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. Domino will dynamically grow and shrink the cluster based on workload demands. This allows more experimentation to accelerate innovation while minimizing compute cost and saving data scientists from wasting precious time on DevOps work.

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.

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Data Center-Ready MLOps
With Domino’s platform as the first Enterprise MLOps software validated and integrated with NVIDIA AI Enterprise, hundreds of thousands of companies already running VMware vSphere® with Tanzu on NVIDIA-Certified Systems™ can cost-effectively build, deploy, manage, and scale accelerated ML workloads using Domino virtualized on industry-standard servers.

“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.”