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Domino Data Lab, a San Francisco, California-based provider of MLOps solutions, today announced that it raised $100 million in a series F funding round led by Great Hill Partners and an expanded partnership with Nvidia, which also participated alongside Coatue Management, Highland Capital Partners, and Sequoia Capital. The proceeds, which bring Domino’s total raised to date to $228 million, will be put toward product development and platform expansion as the company looks to bolster its global customer base, according to CEO Nick Elprin.
MLOps, a compound of “machine learning” and “information technology operations,” is a newer discipline involving collaboration between data scientists and IT professionals with the aim of productizing machine learning algorithms. The market for such solutions could grow from a nascent $350 million to $4 billion by 2025, according to Cognilytica. But certain nuances can make implementing MLOps a challenge. A survey by NewVantage Partners found that only 15% of leading enterprises have deployed AI capabilities into production at any scale.
Domino, which was founded in 2013 by Chris Yang, Matthew Granade, and Elprin, offers a suite of machine learning model management tools that help to combat issues like concept drift, or when the statistical properties a model is attempting to predict shift over time. The company’s virtual workbench enables engineers to leverage existing tools in tracking, reproducing, and comparing experiments while finding, discussing, and reusing work in one place.
“As enterprises advance their adoption of machine learning, they increasingly require an enterprise-grade, open platform to orchestrate and manage these workloads — and Domino perfectly meets this growing market need,” Great Hill Partners’ Derek Schoettle said in a statement.
Using Domino, customers — which include more than 20% of the Fortune 100 — can spin up interactive workspaces on hardware of their choice and scale to more powerful compute resources if necessary. The platform provides a built-in package manager to organize the libraries and tools tapped throughout a project and versioned datasets to track data used during model training and testing. Domino’s reporting features let admins schedule reports to be generated and delivered to stakeholders automatically, while data pipelines handle tasks to keep models up to date.
On the model operations side of the equation, Domino lets customers deploy models as on-demand APIs or export models for deployment on other infrastructure. It can detect data drift and monitor the performance of models in the wild while alerting engineers to those that underperform, with a registry that shows the status of models at a glance. Domino also keeps tabs on compute and measures the business impact of model APIs and apps as it surfaces project health in terms of both progress and potential roadblocks.
Domino says that Nvidia’s investment will enable it to develop product functionality to expand the accelerated computing capabilities of its platform. This includes validating the Domino platform for Nvidia AI Enterprise, a managed collection of software tools designed to accelerate machine learning workloads, so that Domino can run on Nvidia-certified systems from OEM hardware providers. Since 2020, Domino has offered certified enterprise MLOps software for Nvidia DGX systems as a member of Nvidia’s DGX-ready software program.
“AI and data science are new workloads that demand a full-stack solution — one with tools that simplify development and deployment for customers,” Nvidia head of enterprise computing Manuvir Das said in a statement. “Our partnership with Domino Data Lab reflects our commitment to enterprise MLOps. With Nvidia AI Enterprise integration into the Domino platform, customers will be able to easily integrate advanced AI tools into their traditional data center infrastructure.”
Domino, whose customers include Dell, Allstate, UBS, Bristol Meyers, ConocoPhillips, and Lockheed Martin, previously raised $43 million in a funding round coled by Highland Capital Partners and Dell Capital. The company competes with a number of startups in the multibillion-dollar MLOps market, including Iterative.ai, Comet, DataRobot, and Weights and Biases.
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