How do you handle cloud-native storage for large-scale AI/ML models?

Large-scale AI/ML models involve training and inference with massive parameters, requiring efficient storage; cloud-native storage, based on containerization and Kubernetes, provides elastic and scalable solutions. Its importance lies in supporting high-performance computing, reducing costs, adapting to dynamic workloads, and being widely used in scenarios such as AI training and real-time inference.

Core components include object storage (e.g., Amazon S3), distributed file systems (e.g., Ceph), and Container Storage Interface (CSI). Features include high throughput, low latency, and data redundancy to ensure reliability and parallel access. In practical applications, storage solutions are integrated through Kubernetes Operators or CSI Drivers to optimize ML pipeline performance, improve resource utilization, and enhance data persistence.

Implementation steps: (1) Assess requirements: model size, IOPS, and access patterns; (2) Select storage services: such as cloud object storage or local clusters; (3) Integrate into Kubernetes: configure storage classes and persistent volumes; (4) Test elastic scalability and performance. Typical scenarios include distributed training; business values are accelerating development, reducing latency, efficiently scaling model deployment, and enhancing ROI and innovation potential.