Lilly Tech Systems
AI Data Storage Architecture
Design and implement storage architectures optimized for AI workloads. Learn about storage tiers for different AI data types, parallel file systems like Lustre and GPFS, caching strategies to keep GPUs fed with data, data lifecycle management for training datasets and model artifacts, and best practices for building scalable AI storage infrastructure.
What You'll Learn
Complete storage architecture coverage for AI and ML infrastructure.
Storage Tiers
Design multi-tier storage with hot, warm, and cold tiers optimized for different AI data access patterns.
Parallel File Systems
Deploy and configure NFS, Lustre, and GPFS for high-throughput data access from GPU clusters.
Cache Strategies
Implement caching layers to eliminate data loading bottlenecks and maximize GPU utilization.
Data Lifecycle
Manage the lifecycle of training data, checkpoints, model artifacts, and experiment logs efficiently.
Course Lessons
Follow the lessons in order for comprehensive AI storage knowledge.
1. Introduction
AI storage challenges, data access patterns for training and inference, and the storage architecture overview.
2. Storage Tiers
Hot, warm, and cold storage tiers: NVMe, SSD, HDD, and object storage for different AI data types.
3. NFS/Lustre
Deploy shared file systems for AI: NFS for simplicity, Lustre for performance, and GPFS for enterprise.
4. Cache Strategies
Multi-level caching: local NVMe cache, distributed cache, GPU memory prefetching, and data pipeline optimization.
5. Data Lifecycle
Manage training data, checkpoints, model artifacts, and experiment data through their complete lifecycle.
6. Best Practices
Production storage: capacity planning, performance tuning, disaster recovery, and cost optimization.
Prerequisites
What you need before starting this course.
- Basic understanding of storage technologies (block, file, object)
- Familiarity with Linux file systems and mount commands
- Understanding of ML training data loading patterns
- Experience with Kubernetes persistent volumes (helpful but not required)