AI Infrastructure: Cloud Storage Options

High-performance AI and machine learning models require massive amounts of data, making storage a critical bottleneck in infrastructure design. Understanding how to choose and structure your storage can dramatically improve training times and reduce operational costs. This text-only course guides you through the fundamental storage types and configurations needed for modern AI workloads, helping you transition from basic data hosting to building optimized, scalable data pipelines. What you'll learn: 1. Understand foundational storage concepts including object, block, and file storage for AI. 2. Evaluate performance characteristics like throughput, latency, and IOPS for machine learning datasets. 3. Design data pipelines that connect cloud storage directly to training clusters. 4. Optimize storage costs using lifecycle management and tiering strategies. 5. Configure secure access controls and data encryption for sensitive training data. 6. Apply modern best practices for handling unstructured data formats in AI pipelines. You will start with key terminology and foundational storage definitions before exploring practical strategies for selecting and configuring the right storage tier for your specific AI models. This course is designed for beginners in cloud infrastructure, data engineering, or machine learning operations, with no advanced prerequisites required. Start reading today to master the storage foundations that power modern AI systems.