The role storage plays in the AI data cycle
As the artificial intelligence (AI) industry continues to evolve, it will require the development of robust infrastructure to train models and deliver services, which has a major impact on data storage and management. This has significant implications for the amount of data generated and, most importantly, how and where this insight should be stored.
The ability to manage this data efficiently is becoming critical as data requirements increase exponentially due to the continued growth and development of AI tools. Therefore, the storage infrastructure required to support these systems must be able to scale in parallel with rapid advances in AI applications and capabilities.
As AI creates new data and makes existing data even more valuable, a cycle is quickly emerging where increased data generation leads to greater storage needs. This drives further data generation and forms a ‘virtuous AI data cycle’ that drives AI development forward. To fully realize the potential of AI, organizations must not only understand this cycle, but also fully understand its implications for infrastructure and resource management.
Peter Hayles, HDD Product Marketing Manager, Western Digital.
An AI data cycle in six phases
The AI Data Cycle consists of a six-phase framework designed to streamline data processing and storage. The first phase focuses on collecting existing raw data and storage. Data is collected and stored here from various sources, and the analysis of the quality and diversity of the collected data is crucial: it forms the basis for the next phases. Capacity business hard drives (eHDDs) are recommended for this phase of the cycle as they offer the highest capacity per drive and the lowest cost per bit.
In the next phase, data is prepared for intake and the assessment from the previous phase is administered, prepared and transformed for training purposes. To accommodate this phase, data centers are deploying upgraded storage infrastructure – such as high-speed data lakes – to support data for preparation and ingestion. Here, high-capacity SSDs are needed to expand existing HDD storage or create new all-flash storage systems. This provides quick access to organized and prepared data.
Then comes the next phase of training AI models to make accurate projections with training data. This phase typically takes place on powerful supercomputers, requiring specific and powerful storage solutions to work most effectively. Here, high-bandwidth flash storage and enhanced low-latency eSSDs are created to meet the specific needs of this phase and provide the necessary speed and precision.
Then, after training, the inference and prompting phase focuses on creating a user-friendly interface for AI models. This phase involves using an application programming interface (API), dashboards, and tools that combine context with specific data with end-user direction. AI models will then be integrated into Internet and client applications without the need to exchange current systems. This means that maintaining current systems alongside new AI computing will require more storage space.
Here, larger and faster SSDs are essential for AI upgrades in computers, and higher capacity built-in flash devices are needed for smartphones and IoT systems to maintain seamless functionality in real-world applications.
The AI inference engine phase follows, where trained models are placed in production environments to perform the examination of new data, produce new content, or make real-time predictions. At this stage, the engine’s efficiency level is critical to achieving fast and accurate AI responses. To ensure comprehensive data analysis, significant storage performance is essential. To support this phase, high-capacity SSDs can be used for streaming or to model data in inference servers based on scaling or response time needs, while high-performance SSDs can be used for caching.
The final stage is where the new content is created, where insights are produced by AI models and then stored. This phase completes the data cycle by continuously improving the data value for future model training and analysis. The generated content is stored on enterprise hard drives for data center archive purposes and on both high-capacity SSDs and embedded flash devices for AI edge devices, making it immediately available for future analysis.
A self-sustaining data generation cycle
By fully understanding the six phases of the AI data cycle and deploying the right storage tools to support each phase, companies can effectively support AI technology, streamline their internal operations, and maximize the benefits of their AI investment.
Today’s AI applications use data to produce text, video, images, and various other forms of interesting content. This continuous loop of data consumption and generation accelerates the need for performance-oriented and scalable storage technologies to manage large AI datasets and efficiently restructure complex data, driving further innovation.
The demand for suitable storage solutions will increase significantly over time as the role of AI in all operations becomes even more important and integral. As a result, access to data, the efficiency and accuracy of AI models, and larger, higher quality data sets will also become increasingly important. Furthermore, as AI becomes embedded in virtually every industry, partners and customers can expect storage component suppliers to tailor their products to provide a suitable solution for every stage of the AI data cycle.
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