Cloud Storage Solutions for AI and HPC Workloads

Selecting the right storage architecture is critical for running efficient machine learning pipelines and high-performance computing (HPC) workloads. Without the proper storage setup, your computational resources will sit idle waiting for data to load. This text-only course guides you through the foundational concepts of cloud-based storage systems designed specifically for AI. You will learn how to analyze data access patterns, compare different storage classes, and choose the optimal configurations to feed your training models at scale. What you'll learn: - Understand core storage types including object, block, and parallel file systems for AI. - Analyze the unique data access patterns and throughput requirements of machine learning workloads. - Compare performance metrics and cost-efficiency across various cloud storage tiers. - Explore modern data storage patterns like lakehouses and vector storage requirements. - Apply best practices for data ingestion, caching, and preprocessing pipeline integration. The course starts with essential terminology and foundational storage concepts before walking through practical architectural patterns and optimization strategies for large-scale model training. It is designed for beginners, data enthusiasts, and cloud practitioners looking to understand the storage side of AI infrastructure, with no prior cloud architecture experience required. Start building a solid foundation in AI data infrastructure today.