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Learn how to protect the critical data and infrastructure that power AI systems in this 49-minute conference presentation from AI Field Day 7. Discover why foundational AI data often lacks comprehensive protection despite the focus on GPUs and models, and explore the significant challenges in maintaining consistency, accuracy, and recoverability across vast datasets in data lakes, object storage, and lakehouses. Understand how cloud environments have become the "home of AI" while introducing new threats through fragmented services and protection blind spots. Examine HYCU's comprehensive approach to AI data protection that goes beyond traditional backup solutions by capturing entire ecosystems including metadata, views, access policies, and AI-specific formats like enriched JSON and vector databases. Explore how this platform enables traceability and rollback capabilities for AI pipelines, addressing critical issues such as schema drift, corrupted data, and poisoned datasets while aligning backups with model training checkpoints. Gain insights into cost-effective backup strategies using deduplication technologies like DD Boost that achieve up to 40:1 storage savings and support cross-cloud protection to prevent vendor lock-in and catastrophic failures.
