Maximize AI Workload Efficiency with Domino Volumes for NetApp ONTAP

DavidSchulman · ‎2025-06-18

Written By: David Schulman (Domino Data Lab), James Yi (AWS), and Richard Swakla (NetApp)

Many enterprises still struggle to turn innovative AI experiments into scalable, governed business value, often hitting roadblocks in data management and infrastructure. At Domino Data Lab’s May 2025 RevX conference in Philadelphia, we dissected these very challenges, particularly through the lens of demanding sectors like life sciences.

The good news? Collaborative solutions are emerging to directly address these hurdles. We’re thrilled that as part of Domino Data Lab's Spring 2025 Release on June 18th, Domino Volumes for NetApp ONTAP (DVNO) is now Generally Available. This powerful new capability targets the foundational data complexities that can stall enterprise AI.

This post connects recent insights from our RevX discussion and Domino’s 2025 REVelate report to underscore how innovations like DVNO are vital for building resilient and agile AI infrastructure for regulated industries.

The Dual Imperative: Embracing AI's Potential While Grounding in Reality

The RevX discussion highlighted the transformative AI use cases emerging in life sciences, such as accelerated protein structure prediction with technologies like AlphaFold 3, the precision of CRISPR-based gene editing, and novel mRNA applications. These are not just incremental improvements; they represent fundamental shifts in research and development.

However, a core theme, supported by findings from the recent Domino Data Lab "REVelate 2025: From Prototype to Payoff" report, was the pragmatic approach organizations must take.

The report surveying 300+ C-level executives, VPs, and directors involved in AI strategy across North American and European enterprises, reveals that while life sciences companies are at the forefront of AI adoption (with 98% utilizing Generative AI and 95% using agentic AI), there's a clear understanding that realizing substantial ROI is a journey. A significant 63% of life sciences organizations anticipate a return of less than 50% on machine learning investments in the coming year…with just 3% expecting GenAI to deliver returns exceeding 100%.

Modernizing Infrastructure for Intensive AI: Data Across Environments

A significant portion of our RevX discussion focused on the need for robust, foundational capabilities rather than chasing hype cycles. The sheer volume and complexity of data required for use cases such as genomic sequencing and molecular simulations necessitate a strategic approach to compute and data management.

Key considerations include:

Accessible High-Performance Compute: Readily available, powerful compute resources, including GPUs, for training complex models and processing large datasets
Elastic and Scalable Cloud Services: Cloud platforms for elasticity and breadth of services to efficiently manage fluctuating workloads and optimize costs
Strategic Data Management for Hybrid Environments: Hybrid strategies spanning on-premises systems, cloud storage, and heterogeneous research environments to meet compliance requirements in regulated industries

Addressing these complex data environments is where the collaboration between NetApp, Domino, and AWS truly delivers. For data scientists, this means streamlined, self-service access to critical data—wherever it resides—paired with their preferred tools and scalable compute power.

Crucially, IT teams appreciate this approach because it leverages NetApp's intelligent data infrastructure, including ONTAP and its integration with AWS services like Amazon FSx for NetApp ONTAP. This allows IT to manage enterprise data volumes with their standard, trusted practices, ensuring governance and control even across hybrid setups. It’s a win-win: data scientists are unblocked, IT maintains operational integrity, and the result is accelerated research, more efficient AI development, and a welcome reduction in AI sprawl.

Enabling Innovation Through Robust and Integrated Governance

AI governance is not merely a compliance checkbox but a fundamental enabler of trustworthy and scalable innovation. Our panel emphasized that governance frameworks must be integrated into the AI lifecycle, providing transparency and reproducibility without stifling agility. Domino’s report indicates that 66% of organizations are now prioritizing the implementation of integrated AI/ML governance systems.

Domino is designed to embed governance throughout the model development and deployment process. This includes capabilities for automated audit trails, model lineage tracking, and standardized validation workflows, helping organizations innovate responsibly and meet stringent regulatory requirements.

From Potential to Impact: Navigating the Path to AI Value

While the technical ability to deploy AI is improving (88% of organizations reported progress in the REVelate report), the journey to significant business value is ongoing. The report highlighted that 60% of organizations expect less than 50% ROI from ML initiatives in the next year.

For life sciences and other regulated industries, these factors are often compounded by long research and development cycles. However, organizations that invest in strong foundational elements – scalable infrastructure for diverse data environments, integrated governance, and a clear strategy for the end-to-end AI lifecycle – are best positioned to translate AI's potential into lasting impact.

Domino Volumes for NetApp ONTAP is now Generally Available

We’re proud to announce that DVNO is now Generally Available. Now, AI teams can:

Capture one-click, efficient data snapshots for auditability and reproducibility.
Accelerate model training by removing storage bottlenecks through maximized data throughput with minimized latency.
Deliver self-service data science data access under IT control across hybrid environments using trusted NetApp ONTAP infrastructure.