F E A T U R E central to determining the success or failure of cutting-edge technology initiatives.
Addressing these challenges requires more than just adding raw capacity. Instead, businesses are rethinking the lifecycle of their data to ensure it is stored, moved and retained in ways that support the performance demands of AI.
Effective lifecycle management can determine whether training runs are completed on schedule, whether inference workloads deliver results in time and whether overall costs remain sustainable, among other variables.
As a result, storage tiering is taking on a greater role in ensuring data is placed in the most appropriate storage architecture, primarily according to its value and frequency of use. For instance, high-performance systems are reserved for the datasets that must be accessed often or at speed, while less critical data is moved to lower-cost environments. This avoids unnecessary expenditure on premium capacity and helps organisations maintain control over spiralling storage footprints, particularly when they need to balance storage assigned to AI use cases compared to other priorities.
Equally important is the ability to retrieve archived data when required. AI workloads often need to revisit older datasets to refine models or support
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