AI Infrastructure: Storage Options for Machine Learning

Building successful AI and machine learning systems requires more than just powerful compute—it demands a robust, high-performance storage foundation. Without the right storage architecture, your models waste valuable training time waiting for data to load. This text-based course guides you through the essential storage options for AI and High-Performance Computing (HPC) workloads. You will learn how to align storage capabilities with the unique demands of data preparation, model training, and inference. What you'll learn: - Understand foundational storage concepts, including block, file, and object storage for AI workloads. - Analyze storage requirements for each stage of the machine learning lifecycle. - Evaluate key performance metrics like throughput, IOPS, and latency for large-scale training. - Configure high-performance caching and data lake storage structures to minimize GPU idle time. - Explore modern vector database storage needs and hybrid cloud storage architectures. - Select the most cost-effective and scalable storage tiers for long-term AI data retention. You will start with core storage definitions and progress to comparing specific architecture patterns for training and inference pipelines. The written lessons and conceptual walk-throughs guide you through real-world scenarios and optimization strategies without complex prerequisites. This course is designed for beginner cloud architects, system administrators, and data engineers who want to understand the infrastructure side of AI. No prior storage engineering experience is required. Start building a faster, more efficient foundation for your AI projects today.