AI for the cloud

The state of AI and data

Findings from a 2021 McKinsey customer survey indicate that the adoption of artificial intelligence is continuing its steady rise; 56% of all respondents report that they have adopted AI in at least one function, up from 50% in 2020. The companies seeing the biggest bottom-line impact from AI adoption are more likely to follow both core and advanced AI best practices, including MLOps; to move their AI work to the cloud; and to spend on AI more efficiently and effectively than their peers.

In an age when data is at the core of every decision-making process, a data-centric company can better align its strategy with the interests of its stakeholders by using information generated from its operations. This approach involves systematically altering and improving datasets to increase the accuracy of machine learning (ML) applications.

Hybrid cloud

Hybrid cloud is the architecture of choice for AI today, embraced by enterprises worldwide. Many customers are engaging in hybrid cloud AI, some are starting in the cloud for AI and repatriating models to on premises as cloud costs escalate, and others are starting on premises and using the cloud to scale compute resources for inference or training.

You can elevate your cloud with services built for the multicloud world. NetApp’s portfolio of enterprise cloud and data management services is built on three decades of innovation to help customers put their data to work across a wide range of business-critical applications. We’re the only company that provides unified enterprise storage across on premises and the largest public clouds through a single enterprise data management system, NetApp® ONTAP®. Our integrated approach delivers cloud application performance with optimized cloud storage costs, enhanced data protection, security, and compliance. Three of the world’s leading cloud providers—Amazon Web Services (AWS), Microsoft Azure, and Google Cloud—collaborate with NetApp to deliver native solutions in their clouds. NetApp helps you leverage the cloud for faster, more dynamic delivery of AI services and frictionless data movement.

NetApp AI advantages in the hybrid cloud.

NetApp AI cloud solutions

It’s time to build your data fabric—a unified data management environment spanning across edge devices, data centers, and one or more public clouds—so your AI data can be ingested, collected, stored, and protected no matter where it resides. Only then can you optimally train AI, drive ML, and empower the deep learning (DL) algorithms necessary to bring your AI projects to life. As the data authority on hybrid cloud, NetApp delivers AI solutions that remove bottlenecks at the edge, core, and cloud to enable more efficient data collection, accelerated AI workloads, and smoother cloud integration.

The NetApp DataOps Toolkit is a Python library that makes it simple for developers, data scientists, DevOps engineers, and data engineers to perform various data management tasks, such as near-instantaneously provisioning or cloning, or making a NetApp Snapshot™ copy of a data volume or JupyterLab workspace. The DataOps toolkit runs both in the cloud (all major cloud providers) and on premises, and NetApp offers a selection of data movers that allow data for AI to be moved to and from the cloud.

Streamline data science with Amazon FSx for NetApp ONTAP and Iguazio. FSx for ONTAP brings the full capabilities of ONTAP to a native AWS managed service, delivering a consistent hybrid cloud experience. The Iguazio Jupyter Notebook takes advantage of the data protection available in Amazon FSx for ONTAP by mounting the FSx ONTAP volumes to the Jupyter Notebook, or by mounting them as a data directory for the training jobs. The Amazon FSx for ONTAP file system has tight integration with the NetApp DataOps Toolkit. NetApp has also validated Domino Nexus architecture as a new solution supporting the Domino Enterprise MLOps Platform on Amazon FSx for NetApp ONTAP.

NVIDIA DGX Foundry and NetApp is a world-class infrastructure for businesses and their data scientists who need a premium AI development experience without the struggle of building it themselves. The infrastructure includes NVIDIA Base Command software and uses NetApp Keystone^® Flex Subscription, a file-based storage subscription service. Offered as a hosted solution, it includes access to fully managed infrastructure based on the NVIDIA DGX SuperPOD architecture.

Use cases

Technical report TR-4904 details how Azure NetApp Files, RAPIDS AI, Dask, and Azure helps data scientists and cloud administrators bring value to business. This solution follows the lifecycle of an AI/ML application. By leveraging RAPIDS on Dask, distributed training is performed across the Azure Kubernetes Service cluster to drastically reduce the training time when compared to the conventional Python scikit-learn approach. To complete the full cycle, the pipeline is integrated with Azure NetApp Files.

Azure NetApp Files offers various performance tiers. Customers can start with a Standard tier and scale out and scale up to a high-performance tier nondisruptively without moving any data. This capability enables data scientists to train models at scale without performance issues, avoiding any data silos across the cluster.

A supermarket chain leveraged AI in the cloud for predictive pricing with Azure NetApp Files. To improve pricing strategies, the data science team turned to AI and ML. They chose Azure NetApp Files as the data environment in their predictive pricing application platform. The service meets their performance and security requirements and allows data scientists to consume data within minutes without having to rearchitect their Azure Landing Zones.

Learn More

For more information, visit the NetApp AI page and the hybrid cloud page.