# Domino and NetApp Enhance Regulated AI Efficiency with FSx

> Domino Data Lab and NetApp's partnership on Amazon FSx for NetApp ONTAP is transforming how regulated industries scale AI, ensuring compliance while optimizing data governance. Discover how strategic data management supports reliable AI operations.

**Source**: aws.amazon.com | **Published**: 2026-11-03 | **Type**: article

## Key Facts

- Domino's use of FSx for ONTAP improved write speeds by 417%, enhancing operational efficiency.
- Regulated AI requires strict data governance, revealing vulnerabilities in traditional architectures.
- Space-efficient clones reduce storage costs, indicating a competitive edge in resource management.
- Novartis and Merck's migration to FSx for ONTAP highlights growing demand in life sciences for compliance.
- AWS's shared responsibility model emphasizes the need for robust customer data governance strategies.

## Summary

Recent developments in artificial intelligence (AI) governance have highlighted the crucial role of data architecture as organizations transition from pilot projects to full-scale production. The collaboration between Domino Data Lab and NetApp on Amazon FSx for NetApp ONTAP is a significant step in addressing the complexities of regulated AI environments. This partnership aims to ensure that organizations can maintain compliance with stringent data governance requirements while optimizing AI operations.

As AI technologies proliferate, particularly in regulated sectors such as healthcare and finance, the need for a robust data governance framework becomes paramount. Organizations face increasing demands for data access, retention, and traceability, driven by regulatory frameworks like the FDA's electronic-record rules and the European Union's GDPR. The AWS Shared Responsibility Model emphasizes that while AWS secures the cloud infrastructure, customers must manage data security and compliance within their cloud environments. This necessitates a strategic approach to data management that integrates governance into the core of AI operations.

The partnership leverages three key principles to enhance AI governance: keeping compute resources close to governed data, preserving reproducible data states, and enabling workload scaling without duplicating datasets. By adopting a hybrid architecture, organizations can optimize data locality, ensuring that compute resources are utilized where approved data resides. This minimizes transfer delays and reduces the overhead associated with managing multiple copies of datasets. 

Domino’s platform, which integrates with Amazon FSx for NetApp ONTAP, exemplifies this approach. The platform allows data scientists to access approved datasets through self-service workflows while maintaining governance controls. This architecture not only streamlines operational efficiency but also supports compliance by ensuring that all data states are documented and recoverable. The use of point-in-time snapshots and versioning facilitates model lineage and audit preparation, essential for meeting regulatory standards.

The implications for the market are substantial. As regulated organizations increasingly migrate their workflows to cloud environments, the demand for solutions that integrate compliance and operational efficiency will rise. Companies like Novartis and Merck, which are transitioning their clinical workflows to this architecture, exemplify the trend toward embracing cloud solutions that prioritize both performance and governance. The reported improvements in write speeds by 417% and read speeds by 94% on Domino Cloud using FSx for ONTAP underscore the potential for enhanced productivity in regulated AI applications.

As the landscape of AI continues to evolve, organizations must prioritize governance as a foundational element of their data architecture. The integration of Domino’s AI platform with NetApp’s data management capabilities on AWS not only addresses current regulatory challenges but also positions businesses to adapt to future compliance requirements. The ability to measure outcomes at the workload level, including provisioning speed and resource utilization, will be critical for organizations seeking to optimize their AI initiatives.

Looking ahead, the strategic focus on data governance in AI operations will likely influence competitive dynamics in the market. Companies that can effectively manage data compliance while harnessing the power of AI will gain a significant advantage. As more organizations recognize the importance of a cohesive data strategy, the demand for integrated solutions like those offered by Domino and NetApp will likely increase, shaping the future of AI deployment in regulated industries.

## Entities

- **Companies**: NetApp, Domino Data Lab, AWS
- **Products**: Domino Enterprise AI Platform, Amazon FSx for NetApp ONTAP
- **Technologies**: Amazon Elastic Kubernetes Service, Amazon CloudWatch, AWS Lambda
- **People**: Semion Mazor, Alon Goldin, Vaibhav Dhawan, Ben Lin
- **Organizations**: AWS Partner Network, AWS Machine Learning Competency Partners

## Key Concepts

regulated AI, data architecture, governance, data locality, reproducibility, storage efficiency, cloud infrastructure, data management

## Definitions

- **regulated AI**: AI systems that must comply with specific legal and regulatory requirements regarding data access, retention, and traceability.
- **data locality**: The principle of keeping compute resources close to the data they process to comply with data residency requirements.
- **point-in-time state**: A snapshot of data at a specific moment, allowing for reproducibility and auditability of AI model training.
- **writable clones**: Space-efficient copies of datasets that allow teams to branch from a common point while sharing unchanged data blocks.
- **AWS Shared Responsibility Model**: A framework that delineates the security responsibilities of AWS and its customers regarding cloud services.

## Use Cases

- Migrating clinical workflows to FSx for ONTAP
- Using Domino for self-service workflows in regulated environments
- Implementing identity-based access controls with FSx for ONTAP
- Utilizing point-in-time storage snapshots for model lineage
- Employing hybrid architectures for data residency compliance
- Automating capacity adjustments with AWS Lambda

## Frequently Asked Questions

**What is the importance of data architecture in regulated AI?**

Data architecture is crucial in regulated AI as it serves as the governance layer that ensures compliance with data access and retention regulations. Proper architecture helps manage data location, access controls, and reproducibility, which are essential for regulatory compliance.

**How does Domino Data Lab support regulated AI workloads?**

Domino Data Lab provides an Enterprise AI Platform that facilitates the building, deploying, and governing of AI workloads. It integrates with AWS services like FSx for ONTAP to ensure that data management and governance principles are upheld in regulated environments.

**What are the benefits of using Amazon FSx for NetApp ONTAP?**

Amazon FSx for NetApp ONTAP offers a fully managed storage solution that supports efficient data management and governance. It enables features like SnapMirror replication and FlexCache caching, which are essential for maintaining data locality and compliance in regulated AI applications.

**What are writable clones and why are they useful?**

Writable clones are space-efficient copies of datasets that allow teams to work from a common point without duplicating the entire dataset. They are useful for reducing storage consumption and improving operational efficiency in concurrent AI experiments.

**How can organizations measure the effectiveness of their AI workloads?**

Organizations can measure effectiveness by analyzing workload-level metrics such as job startup time, end-to-end runtime, and storage consumed by copies and snapshots. These metrics help assess whether infrastructure decisions lead to improved performance and governance.

## Links

- [Read on Welcome.AI](https://welcome.ai/content/domino-and-netapp-enhance-regulated-ai-efficiency-with-fsx)
- [Original source](https://aws.amazon.com/blogs/apn/scaling-regulated-ai-with-domino-on-amazon-fsx-for-netapp-ontap/)

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Source: Welcome.AI | https://welcome.ai/content/domino-and-netapp-enhance-regulated-ai-efficiency-with-fsx