AI needs your data to thrive, but bringing data held in on-prem and cloud environments together isn’t as simple as pressing a button.

Those different data formats, schemas, and storage mechanisms need harmonising, and network bandwidth constraints and latency issues can significantly impact data integration efforts, especially for real-time AI applications.

HPE’s hybrid cloud is a potential solution that offers a balanced approach to data management. The concept is simple: the hybrid cloud retains critical data on-premises under strict security protocols while the cloud processes less sensitive information.

AI models are also deployable at the edge (on-premises or in local data centres) for low-latency operations while maintaining connections to cloud resources.

AI workloads, especially during training, require high-speed data access. In a hybrid cloud setup, storage solutions must address network bandwidth constraints and latency issues that can significantly impact data integration efforts, especially for real-time AI applications.

AI’s data gorge

AI has an insatiable appetite for data from workloads that involve massive datasets from structured and unstructured formats.

Add in the real-time nature of many AI applications, which demand ultra-low latency and high throughput, and it’s easy to see why AI models push storage systems to their limits.

Traditional storage solutions simply can’t keep up. AI requires unique storage for parallel processing across multiple GPUs, allowing concurrent data access.

In a hybrid cloud environment, you don’t consolidate all your data in a single data centre or fully transition to a hyperscale public cloud.

Instead, data on the edge seamlessly integrates into a hybrid cloud architecture, enabling your AI model to access and use the data as soon as it enters production.

A secure solution

Your organisation’s primary security concern is probably the potential for data breaches during model training or inference, which could expose proprietary algorithms or sensitive customer information.

Public clouds may also introduce challenges in maintaining data sovereignty, as data may be stored or processed in various global locations.

A hybrid approach puts your most sensitive data and critical AI models on-premises, under your direct control, and within your chosen jurisdiction to address data sovereignty concerns head-on and ensure compliance with region-specific regulations like GDPR.

Plus, you can still tap into the scalability and advanced capabilities of public clouds for less sensitive operations or when you need additional computing power for intensive AI tasks – basically, you don’t lose any scalability.

HPE GreenLake offers a hybrid cloud platform that allows organisations to retain control over sensitive data and AI models while still leveraging cloud capabilities. HPE also provides advanced security features tailored for AI workloads in hybrid environments, including robust data encryption, access controls, and monitoring capabilities spanning both on-premises and cloud infrastructures.

To discuss why AI requires a different type of data storage and your data storage requirement please give DSI Technology Solutions a call.