As Artificial Intelligence (AI) technologies continue to expand and the infrastructure to support model training and the launching of new services increases, a key consideration for organisations is how to efficiently store and manage the valuable insight generated.
With AI creating new data and making existing data more valuable, a cycle is quickly emerging, where increased data generation leads to expanded storage needs. This fuels further data generation – forming a “virtuous AI data cycle.” Understanding this AI data cycle is important for organisations wanting to access the power of AI and leverage its capabilities.
The Six Stages of the AI Data Cycle
The AI Data Cycle is a six-stage framework, beginning with the gathering and storing of raw data. In this initial phase, data is collected from multiple sources, with a focus on assessing its quality and diversity, which establishes a strong foundation for the stages that follow. For this phase, high-capacity enterprise hard disk drives (eHDDs) are recommended, as they provide high storage capacity and cost-effectiveness per drive.
In the next stage, data is prepared for ingestion, and this is where insight from the initial data collection phase is processed, cleaned and transformed for model training. To support this phase, data centers are upgrading their storage infrastructure – such as implementing fast data lakes – to streamline data preparation and intake. At this point, high-capacity SSDs play a critical role, either augmenting existing HDD storage or enabling the creation of all-flash storage systems for faster, more efficient data handling.
Next is the model training phase, where AI algorithms learn to make accurate predictions using the prepared training data. This stage is executed on high-performance supercomputers, which require specialised, high-performing storage to function optimally. High-bandwidth flash storage and low-latency, optimised enterprise SSDs (eSSDs) are specifically designed to meet the demanding storage requirements of this intensive training process.
The next phase, inference and prompting, focuses on developing user-friendly interfaces for AI models. This includes application programming interfaces (API), dashboards and tools that contextualise specific data for end-user prompts. During this stage, AI models are integrated into web and client applications without the need to replace existing systems, which creates the need for additional storage to support both legacy and AI-driven systems. To accommodate these upgrades, higher-capacity, faster SSDs are necessary for AI-enhanced computers, while higher-capacity embedded flash devices are needed for smartphones and IoT devices.
The AI inference engine stage follows, where trained models are put into production to analyse incoming data, generate new content, or provide real-time predictions. The efficiency of the inference engine is vital for ensuring fast and accurate AI responses. High-capacity SSDs are well-suited for streaming or modelling data into inference servers based on scalability and response time requirements, while high-performance SSDs are used for caching to enhance the overall system performance.
In the final stage, AI models generate new content and insights, which are then stored. This stage closes the loop in the data cycle, contributing to ongoing improvement by enhancing the value of data for future training or analysis by subsequent models. The generated content is stored on enterprise hard drives for data center archiving, while high-capacity SSDs and embedded flash devices are used for storage in AI edge devices.
The Self-Sustaining Cycle of Data Generation
By understanding these six stages of the AI data cycle and having the right tools in place, businesses can better sustain the technology to perform internal business functions and capitalise on the benefits AI offers.
Today’s AI systems transform data into various outputs – text, video, images, and more – creating a dynamic loop of data and production. This cycle amplifies the demand for high performance, scalable storage solutions that can handle massive datasets and streamline complex data processing, thereby propelling continued AI innovation.
The Demand for storage is significantly increasing as its role becomes more prevalent. Access to data, the efficiency and accuracy of AI models, and larger, higher-quality datasets will increasingly become more important. Additionally, as AI becomes embedded across nearly every industry, customers and partners can expect to see storage component providers tailor products to each stage of the AI data cycle.
About the Author
Peter Hayles is Product Marketing Manager HDD at Western Digital. At Western Digital we create data storage solutions that power the technology of today and inspire the innovations of tomorrow.
Featured image: Adobe Stock
