DataCenterWorld2025驾驭AI数据存储优化策略分析报告英文版页_34mb

AI Data Storage: Key Strategies and Optimization

AI integration into data pipelines necessitates tailored storage solutions to ensure efficient model training and scalability. Follow these key principles:

1. Alignment with Business Goals: Integrate AI processing within the project pipeline to ensure models are efficient and storage caters to specific needs while being scalable.

AI Data Pipeline Storage Needs

• Data Collection & Preparation:

  • Centralize raw data (often in Data Lakes).
  • Requires high aggregate performance (matching internet speeds), capacity scalability, cost efficiency, redundancy (backups/repl), and interoperability. Cloud object storage (e.g., Azure) is prevalent.

• Model Training & Processing:

  • Feeds curated datasets to GPUs.
  • Essential needs: High throughput, fast read/write speeds, reliability (steady data stream, handling large checkpoints), minimal management (local NVMe SSDs), and security/privacy (especially for internal data). Usually done in the cloud using multi-GPUs.

• Inference & Model Deployment:

  • Hosts the final model for predictions/decisions.
  • Needs high-speed, mission-critical storage for low latency and real-time performance. Low latency storage (e.g., NVMe drives) enables quick results across diverse environments (edge, data centers). Online bulk capacity object storage popular.

• Archiving:

  • Long-term persistence of artifacts, raw data, and model versions.
  • Prioritizes robust durability, online retrieval capability, and cost-effectiveness due to infrequent access. Hybrid cloud/edge convergence underway.

Cloud vs. On-Premises

• Hybrid approach common (especially train-in-cloud, infer-on-prem/edge).
• Cloud storage: Highly scalable, flexible for different pipeline stages, various storage types available.
• On-Premises storage: Often limited capacity and performance, harder to tune for diverse workloads.
• Each pipeline stage (data prep, training, inference) influences optimal storage type. Key considerations include performance, cost efficiency, data security, redundancy, and interoperability for both cloud and on-prem solutions.

The Importance of Scalability and Trends

Efficient AI pipelines employ various storage types. Ongoing development focuses on optimizing the entire AI data process, with high-throughput SSD-based distributed storage critical for continuous model training and low-latency storage for real-time inference. Early pipeline steps consume significant project time (est. 70%). Key trends include enhanced data discovery, classification, version control, and lineage, followed by shifts away from generic local SSDs towards specialized file systems/cloud-optimized storage. Storage security/privacy and adherence to compliance are paramount throughout the pipeline.

Optimizing storage can streamline operations and enhance AI project effectiveness. hybrid setups and advanced technologies will likely dominate.