Domino Data Lab Recognized as Visionary for Data Science Innovation in 2025

Domino Data Lab Continues to Shine in AI Governance and Innovation



In a remarkable achievement, Domino Data Lab, a leading provider of Enterprise AI solutions, has once again earned recognition as a Visionary in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms. This acknowledgement marks the second consecutive year for Domino, demonstrating its unwavering commitment to innovation and excellence in an ever-evolving AI landscape.

Establishing Trust in Enterprise AI



Trusted by major players in sectors such as life sciences, finance, and public services, Domino Data Lab has positioned itself as a strategic partner for enterprises navigating complex, highly regulated environments. The company's ability to provide effective governance, hybrid cloud orchestration, and cutting-edge generative AI capabilities has been pivotal in its recognition as a Visionary.

Nick Elprin, co-founder and CEO of Domino Data Lab, shared, "Enterprises trust Domino to move faster, reduce risk, and deliver real-world AI impact. This recognition reinforces what our customers already know—we help them cut time to AI value and streamline governance."

The Magic Quadrant Report evaluated 16 vendors based on two key criteria: Completeness of Vision and Ability to Execute. Being placed in the Visionaries Quadrant signifies Domino’s strength in anticipating the evolving needs of enterprises as they scale their AI practices amidst increasing regulatory scrutiny.

Innovative Solutions for Modern Challenges



The sustained acknowledgment from Gartner reflects the significant strides that Domino Data Lab has made in 2024 and beyond. This includes a suite of new capabilities designed to enhance the AI lifecycle, showcasing the company as the only integrated platform for developing and managing AI across diverse infrastructures.

Notable advancements include:
  • - Domino Governance: This groundbreaking built-in solution automates policy enforcement throughout the AI lifecycle, significantly reducing validation timelines—by up to 70%—especially for model risk management and statistical computing in life sciences.
  • - Support for NVIDIA NIM™ microservices: This feature streamlines the deployment of generative AI workloads, ensuring seamless performance across hybrid cloud environments.
  • - Domino Volumes for NetApp ONTAP (DVNO): This capability allows for rapid, compliant access to enterprise data, optimizing AI data processing time by as much as 50% while maintaining detailed traceability.
  • - Deployment to Amazon SageMaker: This functionality facilitates cost-effective inference workloads in public cloud settings, enhancing flexibility for AI teams.

All these innovations reside within the Domino Nexus architecture, which guarantees a cohesive management layer for multi-cloud and on-premises environments. Such flexibility empowers global enterprises to optimize AI wherever their data resides, ensuring performance, compliance, and economic efficiency at scale.

Expanding Footprint in Regulated Industries



As awareness of the significance of data governance rises, Domino is expanding its presence in sectors where compliance and auditability are critical, including life sciences, insurance, and financial services. The previous fiscal year underscored this trend, as Domino Cloud's annual recurring revenue in life sciences soared by over 12X, largely attributed to the rapid acceptance of its managed SaaS solutions aligned with key regulatory standards such as GxP and HIPAA.

In conclusion, the recognition from Gartner signifies not just a milestone for Domino Data Lab but reflects an industry-wide acknowledgement of the company's role in shaping a robust, compliant, and innovative AI-driven future. As enterprises continue to adopt AI technologies, Domino is poised to remain a key player in enabling businesses to harness AI effectively and responsibly.

For those interested in exploring the full details, the complete 2025 Gartner Magic Quadrant for Data Science and Machine Learning Platforms can be accessed here.

Topics Business Technology)

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