【DataCenterWorld】2025驾驭AI数据存储优化策略分析报告页_34mb

Mastering AI Data Storage: Strategies for Optimization

Integrating AI processing into your project data pipeline ensures models are trained efficiently and storage aligns with business goals. Effective optimization involves a comprehensive data management strategy beyond hardware selection, addressing the immense data needs of large language models (LLMs).

Introduction: AI Data Pipeline Stages

The AI data pipeline includes stages with specific storage needs:

1. Data Collection & Pre-processing: Raw, unstructured data stored in data lakes before being curated for training.
2. Model Training & Processing: Curated datasets fed into GPUs, storing training artifacts like hyperparameters and validation data.
3. Inference & Model Deployment: Mission-critical storage for hosting trained models to make predictions.
4. Project Archiving: Long-term storage for training datasets, model versions, and raw data, ensuring accessibility.

Cloud vs. On-Premises Storage

The choice depends on the project's needs:

• Cloud storage offers scalability, cost-efficiency, and various options for different pipeline stages.
• On-premise storage is often limited, leading to solutions that may not optimize all workloads.
• Hybrid setups are common, training in the cloud and inferring on-premises or at the edge.

Storage Requirements by Stage

Stage 1: Data Collection & Pre-processing

• High aggregate performance to stream data efficiently from remote centers and IoT devices.
• Scalability and cost-efficiency critical for handling growing datasets.
• Redundancy and security essential; cloud object storage (e.g., Azure) is prevalent, using HDDs or SSDs.

Stage 2: Model Training & Effective LLM/RAG Training

• Requires high I/O throughput for feeding large datasets to GPUs during training.
• Features to obscure PII are important for sensitive data.
• Security measures and cataloging capabilities ensure data safety and accessibility.

Stage 3: Effective AI Inference & Deployment

• Low-latency storage supports real-time performance.
• Scalable storage optimizes data access for edge or data center environments.

Stage 4: Project Archiving

• Robust, durable storage for long-term integrity and accessibility.
• Cost-effective options like cloud object storage or active archives using tapes (e.g., HDD-based) suit infrequent access.

The Importance of Scalability and Evolution

Scalability is key to align storage with evolving AI demands. Strategies like optimizing storage types and leveraging hybrid solutions can streamline data movement and processing. As AI matures, the focus shifts to core storage requirements ensuring efficiency, security, and scalability.

Conclusion

Effective AI requires optimal data storage strategies that support the entire data pipeline. Cloud and on-premise solutions provide flexible options that help align inventory with goals. The AI landscape's evolution underscores the need for adaptable, secure, and scalable storage systems.