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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 traditional backup solutions fall short when dealing with AI-specific workflows and the unique challenges posed by vast, varied datasets generated during AI implementation across data lakes, object storage, and lakehouses. Explore the risks associated with fragmented cloud services and blind spots in current protection measures, particularly as cloud environments become the primary home for AI operations. Understand how comprehensive protection strategies must extend beyond raw data to include metadata, views, access policies, and AI-specific formats like enriched JSON and vector databases. Examine advanced backup approaches that align with AI pipeline stages such as model training checkpoints while maintaining consistency across fragmented and asynchronous data processes. Investigate cost-effective solutions using deduplication technologies like DD Boost that can achieve up to 40:1 storage savings and support cross-cloud backups to prevent vendor lock-in. Gain insights into addressing critical AI challenges including schema drift, corrupted data, and poisoned datasets through proper traceability and rollback capabilities, ultimately reducing operational risk in rapidly evolving AI workload environments.
