As the Artificial Intelligence (AI) industry continues to mature, the development of a robust infrastructure to train models and provide services is necessary, which has a major impact on data storage and management. This has important implications for the amount of data generated and, most importantly, for how and where this information is stored.
The ability to manage this data efficiently is becoming critical as data requirements increase exponentially due to the continued growth and development of artificial intelligence tools. Therefore, the storage infrastructure needed 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 quickly emerges where more data generation leads to greater storage needs. This drives increased data generation, forming a “virtuous AI data cycle” that drives AI development. To fully realize the potential of AI, organizations must not only take advantage of this cycle, but also fully understand its implications for infrastructure and resource management.
Peter Hayles, Director of HDD Product Marketing, Western Digital.
A six-stage AI data cycle
The AI data cycle consists of a six-stage framework designed to optimize data handling and storage. The first stage focuses on collecting and storing existing raw data. Here data is collected and stored from various sources, and analysis of the quality and diversity of the data collected is essential: it lays the foundation for the next stages. For this stage of the cycle, capacity enterprise hard disk drives (eHDDs) are recommended as they offer the highest capacity per drive and lowest cost per bit.
The next stage is where the data is prepared for intake and the assessment from the previous stage is administered, prepared and transformed for training purposes. To adapt to this stage, data centers are applying enhanced storage infrastructure, such as fast data lakes, to support data for preparation and ingestion. In this case, high-capacity SSDs are needed to enhance existing HDD storage or to create new all-flash storage systems. This ensures 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 occurs on high-performance supercomputers, requiring specific, high-performance storage solutions to operate as efficiently as possible. Here, high-bandwidth flash storage and enhanced low-latency eSSDs are created to meet the specific needs of this stage, providing the necessary speed and accuracy.
Then, after training, the inference and prompting stage focuses on creating an easy-to-use interface for the AI models. This stage incorporates the use of an application programming interface (API), dashboards, and tools that combine context with specific data with prompts for the end user. The AI models will then be integrated into the Internet and into customer applications without needing to swap current systems. This means that maintaining current systems alongside new AI computing will require more storage.
In this case, larger and faster SSDs are essential for AI upgrades in computers, and higher capacity integrated flash devices are needed for smartphones and IoT systems to maintain seamless functionality in the world's applications. real.
The AI inference engine stage follows, where trained models are placed in production environments to examine new data, produce new content, or provide real-time predictions. At this stage, the level of engine efficiency is essential to achieve fast and accurate AI responses. Therefore, to ensure complete data analysis, significant storage performance is essential. To support this stage, high-capacity SSDs can be used for streaming or to model data in inference servers based on scale or response time needs, while high-performance SSDs can be used for caching.
The final stage is where new content is created, with information produced by AI models and then stored. This stage completes the data cycle, continually improving the value of the data for future model training and analysis. The generated content will be stored on enterprise hard drives for data center archiving purposes and on both high-capacity SSDs and embedded flash devices for AI devices, making it available for future analysis.
A self-sustaining data generation cycle
By fully understanding the six stages of the AI data cycle and employing the right storage tools to support each phase, businesses can effectively sustain AI technology, optimize their internal operations, and maximize the benefits of their AI investment.
Today's AI applications use data to produce text, videos, images, and other forms of engaging content. This continuous cycle of data consumption and generation accelerates the need for scalable, performance-based storage technologies to manage large AI data sets and refactor complex data efficiently, driving greater innovation.
The demand for suitable storage solutions will increase significantly over time as the role of AI in all operations becomes even more prevalent 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. Additionally, as AI becomes integrated into nearly every industry, partners and customers can expect to see storage component vendors adapt their products so that there is a suitable solution at each and every stage of the data cycle. of AI.
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