Data Center World：2025驾驭AI数据存储：优化策略分析报告（英文版）（13页）.pdf

1、Mastering AIData StorageDiscover how integrating AI processing into your projectdata pipeline can ensure your AI models are optimallytrained and your storage solutions perfectly aligned withyour goals.Read more to learn how you can make themost of your AI investments.Source:Data Center KnowledgeStra

2、tegies for OptimizationOptimizing storage for AI goes beyond selecting the right hardware;itdemands a comprehensive data management strategy to handle theimmense data needs of large language models(LLMs).By treating AI processing as an integral part of the project data pipeline,enterprises can ensur

3、e their generative AI models are trained efficientlyand that storage solutions are tailored to their specific needs.Prioritizingdata storage requirements is crucial for businesses to create AI models thatare not only effective but also scalable.INTRODUCTIONAI DATA PIPELINE STAGES In an AI data pipel

4、ine,various stages align with specific storage needs to ensure efficient data processingand utilization.Here are the typical stages along with their associated storage requirements:Aligned to Storage NeedsData Collection&Pre-processingThe storage where raw,often unstructured data is collected and ce

5、ntralizedoften inData Lakesbefore being cleaned and transformed into curated datasets,ready fortraining processes.Model Training&ProcessingThis storage stage feeds curated datasets into GPUs for processing and stores essentialtraining artifacts like hyperparameters,run metrics,validation data,and th

6、e finalinferencing model.Storage needs vary based on whether youre developing a largelanguage model(LLM)from scratch or enhancing an existing model,such as a regenerativeaugmented generation(RAG).The mission-critical storage where the trained model is hosted to make predictions ordecisions based on