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Global: AI Development Relies on Data Management’

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AI Development Relies on Data Management’
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Experts assert that the development of Artificial Intelligence (AI) critically depends on effective data management, underscoring the necessity for efficient storage solutions.

In a recent presentation titled “The AI Data Cycle: Understanding the Optimal Storage Mix for AI Workloads at Scale,” Ghassan Azzi, Sales Director at Western Digital, highlighted the pivotal role of storage technology in the AI industry. Azzi explained that as the AI industry constructs extensive infrastructure to train models and deliver AI services, the demand for advanced storage solutions becomes increasingly significant.

“This infrastructure has significant implications for data storage, emphasizing that storage technology plays a crucial role in the cost and power efficiency of various stages,” Azzi stated.

Azzi introduced the concept of the “AI data cycle,” describing it as a continuous loop of data generation and storage that drives the need for sophisticated, cost-effective, and power-efficient storage solutions.

“As AI systems process and analyze data, they create new data, which is often stored due to its usefulness or entertainment value. This leads to a virtuous cycle where increased data generation fuels expanded data storage, further fueling data generation,” he explained.

Azzi stressed that this ongoing cycle of data generation and consumption is accelerating the demand for high-performance and scalable storage technologies.

Ed Burns, Research Director at IDC, noted the significant implications for storage, emphasizing that access to data affects the speed, efficiency, and accuracy of AI models, especially as larger and higher-quality datasets become more common.

“As AI technologies become embedded across various industries, storage component providers are increasingly tailoring their products to meet the needs of each stage in the AI data cycle,” Burns said.

He added that ongoing innovations in storage technology are crucial for managing large AI datasets and ensuring the efficient re-factoring of complex data, which, in turn, drives further advancements in AI.

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