AI Infrastructure: Cloud Storage Options for ML Workloads

Designing infrastructure for artificial intelligence requires a deep understanding of how data flows through your system. Choosing the wrong storage option can lead to massive bottlenecks, slow model training times, and runaway cloud costs. This text-based course equips you with the foundational knowledge to evaluate, select, and implement the right cloud storage solutions for AI and High-Performance Computing (HPC) workloads. You will learn how to align storage characteristics with the unique demands of each phase in the machine learning lifecycle. What you'll learn: - Understand the fundamental storage requirements for AI, machine learning, and HPC workloads. - Compare different cloud storage types, including object storage, block storage, and parallel file systems. - Select the optimal storage tier for each stage of the ML lifecycle, from data preparation to model training and deployment. - Analyze the storage demands of modern AI architectures, including vector databases and retrieval-augmented generation (RAG) systems. - Apply cost-optimization and performance-tuning strategies to prevent data bottlenecks during intensive training runs. The course starts with essential terminology and core cloud storage concepts before guiding you through practical scenarios and workload requirements. You will progress through written explanations, architectural breakdowns, and conceptual exercises designed to solidify your decision-making skills. This course is designed for beginner cloud architects, aspiring data engineers, and IT professionals looking to understand the storage side of AI infrastructure. No prior cloud storage experience is required. Start reading today to build a solid foundation in cloud storage architecture for modern AI.