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Addressing AI and GenAI storage demands with Amazon FSx for NetApp ONTAP

There’s a huge paradigm shift taking place right now. Artificial intelligence and machine learning (AI/ML) capabilities aren’t just useful in theory—they’re becoming necessities.

But AI-driven applications often introduce big changes in how organizations operate as they jockey to stay ahead of the competition. Yet at the core of every AI/ML effort is one shared common factor: data.

Therefore, the backbone of a successful AI/ML initiative is effective data management. Amazon FSx for NetApp ONTAP can help you with that. This blog explores the special data requirements of AI/ML workloads and how you can use FSx for ONTAP to address them.

Read on for more, or jump down using these links:

The role of data in the AI and GenAI landscape

AI and, more recently, generative AI (GenAI), are part of an evolving and dynamic IT landscape that has been growing in momentum for the last few years. That momentum has gone into overdrive since the release of OpenAI’s ChatGPT in 2022.

Along with this growth, the role of data management in the success of AI/ML initiatives has become increasingly relevant:

  • Data is the backbone of the AL/ML algorithms. Data fuels the ability of AI/ML algorithms to analyze patterns and make informed decisions. To get the best performance out of AI models, this data must be managed effectively.
  • It takes seamless coordination to make the right data available at the right time in the right format. The synergy between data management and AI development is important. Offering a strong foundation for advanced learning and generative capabilities improves the precision and efficacy of AI models and helps accelerate innovation.
  • Bursting to the cloud and hybridity. Data hosted in on-premises storage systems might need to integrate with cutting-edge, cloud-based AI/ML solutions. But the sheer size of this data—and, in some cases, its sensitivity—can make it difficult to move to the cloud. Hybrid architectures and cloud-bursting technologies can help bridge the gap.

The challenges of AI/ML data management

You might come across several challenges while managing data for AI/ML solutions. Tackling these challenges would require careful consideration of strategic solutions:

  • Performance. Processing and retrieving data quickly and efficiently are crucial for optimal AI/ML performance. A storage layer can become a performance bottleneck when you’re working with large-scale AI model datasets.
  • Cost optimization. With the scale of data required to train AI/ML models, it can be extremely challenging to balance the costs for processing, storing, and moving that data. Finding economical solutions without sacrificing the quality of data processing is essential for efficient AI/ML data management. You’ll need to strike a careful balance between cost and processing power.
  • Scalability. To improve accuracy in an AI model, the dataset always needs to grow. As that happens, scalability becomes a concern. It can be challenging to accommodate ingesting more data—you would need a solution capable of handling petabyte-scale data from various sources, which generally isn’t easy to find.
  • Business continuity. Like any line-of-business workload, maintaining business continuity for your AI/ML models requires continuous access to data. Any downtime or interruptions to data access can hinder AI/ML applications and any business processes that depend on them.
  • Data security. A lot of effort is required to establish and maintain the security guardrails that shield sensitive data from unauthorized access and breaches. You might also be required to account for this data and how it’s being used to align with data regulations.
  • Data migration and mobility. Although many AI/ML datasets are stored in on-premises systems, the cloud offers a range of AI/ML application development services. To take advantage of them, it’s crucial to find a reliable method for getting that data to the cloud. Migration missteps can open the door to disruptions and data integrity issues.

Data management in the era of advanced AI/ML solutions calls for finding a balance among performance, cost, scalability, continuity, security, and migration considerations.

It’s not an easy ask, but AWS and NetApp have a solution that can help: Amazon FSx for NetApp ONTAP.

AI storage is powered by FSx for ONTAP

Amazon FSx for NetApp ONTAP is a fully managed data management service that integrates the power of AWS with NetApp® ONTAP® technology. FSx for ONTAP offers multiple features that can benefit AI workloads, enabling organizations to efficiently manage, process, and analyze the large volumes of data crucial for AI/ML, with best-in-class performance, scalability, security, and flexibility.

Let’s take a look at some of the FSx for ONTAP features that enhance AI workloads:

  • High performance. FSx for ONTAP delivers high-speed data access that’s pivotal for time-sensitive AI application algorithms and data analytics. The quality-of-service controls built into FSx for ONTAP give you consistent performance tailored to AI needs.
  • Scalability and security. With FSx for ONTAP, you can benefit from virtually unlimited storage, which in turn enables you to ingest more data and improve the accuracy of AI models. The storage infrastructure can grow seamlessly as the needs of your AI initiatives expand.

    With the help of NetApp SnapLock® technology, FSx for ONTAP provides immutable write-once, read-many (WORM) storage. That keeps your data tamperproof and protects it from malware attacks.

    Plus, as a fully integrated part of AWS, FSx for ONTAP can use AWS security guardrails so that only authorized personnel and applications can access the AI datasets.
  • Data protection and resilience. FSx for ONTAP combines features to help prevent costly downtime in your AI workflows:
    • Multi-AZ high availability protects from zonal failures and provides the lowest possible recovery objectives.
    • NetApp Snapshot™ technology creates point-in-time copies of your data to protect from data loss.
    • With NetApp SnapMirror® data replication technology, FSx for ONTAP will copy your data to a backup file system across regions, and it helps you recover your data in case of data loss or disaster.
  • Multiprotocol data access. FSx for ONTAP supports simultaneous and multiprotocol access. Whether you’re using iSCSI for block-level storage, SMB for Windows environments, or NFS for Linux-based applications, this multiprotocol compatibility allows data to be accessed and shared flexibly, fostering interoperability between your AI tools and devices.
  • Service integration. FSx for ONTAP is delivered as a first-party service that’s tightly integrated with the AWS Identity and Access Management service. This integration enables specialized optimization of AI/ML workloads, all while reinforcing security and ease of access.
  • AI/ML workflow integration. With NetApp FlexClone® technology, FSx for ONTAP can create instant, zero-cost, writable thin clones of any dataset, to support automated, reproducible pipelines specifically designed for AI/ML processes.

    You can also benefit from tight integration with coding tools: NetApp DataOps Toolkit automates complex data management tasks, and Jupyter Notebook helps data scientists collaborate on AI/ML application development and boosts data availability and integrity during AI model training.
  • Seamless data migration. Lift-and-shift migrations to FSx for ONTAP are easy using SnapMirror to migrate from ONTAP systems; for other environments, you can use NetApp BlueXP™ copy and sync (NetApp Cloud Sync) or AWS DataSync.
  • Hybrid environment for AI. FSx for ONTAP is fully interoperable with your on-premises data on ONTAP systems, so you can connect them to foundational models in the cloud to build your own GenAI model. It offers cloud bursting through NetApp FlexCache® technology and fosters data collaboration through BlueXP edge caching (NetApp Global File Cache).
  • Kubernetes integration. FSx for ONTAP offers persistent storage for containerized applications. It also integrates with Amazon EKS (Elastic Kubernetes Service) through the NetApp CSI (Container Storage Interface) provisioner, NetApp Astra™ Trident.
  • Cost savings. Given the scale of AI datasets, managing their costs is important. NetApp efficient Snapshots, data tiering to lower-cost capacity storage, thin-clone copies, and NetApp storage efficiency features—including thin provisioning, data deduplication, compression, and compaction—combine to bring down the overall cost of storage you consume without compromising performance.

Building GenAI data pipelines with FSx for ONTAP

Most AI models, and especially GenAI models, depend on huge amounts of unstructured data. FSx for ONTAP specializes in handling unstructured data.

FSx for ONTAP also eliminates the need for creating multiple full copies of data to be used with different models. With Snapshot technology, you can use multiple copies of data with minimal storage footprint—and without data duplication. Thus, you can innovate without wasting data and access it flexibly, regardless of the protocols being used.

How a legal technology company’s AI/ML capabilities benefit from FSx for ONTAP

For an example of how FSx for ONTAP is powering the data behind AI/ML models, let’s take a look at one customer success story.

This customer is a legal technology company specializing in mortgage automation. The company helps lenders, servicers, and investors improve mortgage document processing workflows to achieve faster turnaround and cost savings.

The company’s AI-powered application was built on large language model (LLM) architecture for extracting information and insights from structured and unstructured documents. That AI platform’s software was built on top of Amazon EKS. The company needed a storage solution that could offer compatibility with Spot instances and provide rapid access to thousands of files without affecting performance.

The company was able to meet its AI system’s demands with FSx for ONTAP:

  • High performance. FSx for ONTAP provided the company with high-performance file sharing across different Kubernetes pods, which was essential for its AI solution.
  • Seamless migration. The transition to FSx for ONTAP was done effortlessly without altering applications or workflows.
  • Cost effectiveness. FSx for ONTAP proved to be a cost-effective solution, while addressing the limitations of other solutions. Overall, the successful deployment of FSx for ONTAP not only enhanced the file-sharing capabilities, but also reduced storage costs by up to 50%.

AI innovation needs innovative storage

AI/ML and GenAI solutions are transforming the world as we know it at a breakneck speed. Amazon FSx for NetApp ONTAP provides the high-performance storage you need if you’re going to keep up—without compromising on speed, agility, or performance.

Whether you’re dipping your toe in AI solutions or you’re well advanced in your AI journey, FSx for ONTAP can take your AI/ML storage to the next level.

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Product Marketing Manager