Data Infrastructure's Impact on AI Value Creation

Watch a comprehensive technical presentation from Tech Field Day where Ace Stryker explores the fundamental relationship between data infrastructure and AI value creation. Dive deep into the challenges of AI model training, illustrated through practical examples like AI-generated images with errors that demonstrate the importance of quality training data. Learn about the five-stage AI data pipeline - from ingestion to archiving - with detailed explanations of data and performance requirements at each stage. Understand the evolving landscape of distributed AI workloads across core data centers, regional facilities, and edge computing environments. Explore real-world applications of edge AI deployment in medical imaging and autonomous driving, along with the associated challenges of power constraints, space limitations, and serviceability requirements. Discover how storage solutions, particularly QLC SSDs, are adapting to meet the demands of modern AI applications while optimizing performance, energy efficiency, and cost effectiveness in both core and edge deployments.